System and method for generating a location estimate using non-uniform grid points

ABSTRACT

The location of a wireless mobile device may be estimated using, at least in part, one or more pre-existing Network Measurement Reports (“NMRs”) which include calibration data for a number of locations within a geographic region. The calibration data for these locations is gathered and analyzed so that particular grid points within the geographic region can be determined and associated with a particular set or sets of calibration data from, for example, one or more NMRs. Received signal level measurements reported by a mobile device for which a location estimate is to be determined may be compared with the data associated with the various grid points to estimate the location of the mobile device.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority benefit to and herebyincorporates by reference in its entirety co-pending U.S. ProvisionalPatent Application Ser. No. 60/899,379 filed on 5 Feb. 2007.

BACKGROUND

The present subject matter is directed generally towards a system andmethod for estimating the location of a wireless mobile device that isin communication with a wireless communications network. Morespecifically, the present subject matter relates to the problem ofestimating the location of a wireless mobile device using informationfrom one or more Network Measurement Reports (“NMRs”) which may begenerated by a wireless communications network or the mobile device.

As is well known in the art, the use of wireless communication devicessuch as telephones, pagers, personal digital assistants, laptopcomputers, anti-theft devices, etc., hereinafter referred tocollectively as “mobile devices”, has become prevalent in today'ssociety. Along with the proliferation of these mobile devices is thesafety concern associated with the need to locate the mobile device, forexample in an emergency situation. For example, the FederalCommunication Commission (“FCC”) has issued a geolocation mandate forproviders of wireless telephone communication services that puts inplace a schedule and an accuracy standard under which the providers ofwireless communications must implement geolocation technology forwireless telephones when used to make a 911 emergency telephone call(FCC 94-102 E911). In addition to E911 emergency related issues, therehas been increased interest in technology which can determine thegeographic position, or “geolocate” a mobile device. For example,wireless telecommunications providers are developing location-enabledservices for their subscribers including roadside assistance,turn-by-turn driving directions, concierge services, location-specificbilling rates and location-specific advertising.

Currently in the art, there are a number of different ways to geolocatea mobile device. For example, providers of wireless communicationservices have installed mobile device location capabilities into theirnetworks. In operation, these network overlay location systems takemeasurements on radio frequency (“RF”) transmissions from mobile devicesat base station locations surrounding the mobile device and estimate thelocation of the mobile device with respect to the base stations. Becausethe geographic location of the base stations is known, the determinationof the location of the mobile device with respect to the base stationpermits the geographic location of the mobile device to be determined.The RF measurements of the transmitted signal at the base stations caninclude the time of arrival, the angle of arrival, the signal power, orthe unique/repeatable radio propagation path (radio fingerprinting)derivable features. In addition, the geolocation systems can also usecollateral information, e.g., information other than that derived forthe RF measurement to assist in the geolocation of the mobile device,i.e., location of roads, dead-reckoning, topography, map matching, etc.

In a network-based geolocation system, the mobile device to be locatedis typically identified and radio channel assignments determined by (a)monitoring the control information transmitted on radio channel fortelephone calls being placed by the mobile device or on a wirelineinterface to detect calls of interest, i.e., 911, (b) a location requestprovided by a non-mobile device source, i.e., an enhanced servicesprovider. Once a mobile device to be located has been identified andradio channel assignments determined, the location determining system isfirst tasked to determine the geolocation of the mobile device and thendirected to report the determined position to the requesting entity orenhanced services provider.

The monitoring of the RF transmissions from the mobile device orwireline interfaces to identify calls of interest is known as “tipping”,and generally involves recognizing a call of interest being made from amobile device and collecting the call setup information. Once the mobiledevice is identified and the call setup information is collected, thelocation determining system can be tasked to geolocate the mobiledevice.

While the above-described systems are useful in certain situations,there is a need to streamline the process in order to efficiently andeffectively handle the vast amount of data being sent between thewireless communications network and the large number of mobile devicesfor which locations are to be determined. In this regard, the presentsubject matter overcomes the limitations of the prior art by estimatingthe location of a wireless mobile device using, at least in part, one ormore pre-existing Network Measurement Reports (“NMRs”) which includecalibration data for a number of locations within a geographic region.The calibration data for these locations must be gathered and analyzedso that particular points (e.g., “grid points”) within the geographicregion can be determined and associated with a particular set or sets ofcalibration data from, for example, one or more NMRs. Then, the receivedsignal level measurements reported by the mobile device to be geolocatedmay be compared with the data associated with the various grid points toestimate the location of the mobile device. The performance of agrid-based pattern matching system such as that disclosed herein istypically dependent on stored received signal level measurements thataccurately reflect the levels that are likely to be reported by themobile device to be located. These grid points do not necessarily haveto be part of a uniform grid and usually will not be uniformlydistributed throughout the geographic region. These non-uniform gridpoints (“NUGs”), once determined, can be assigned geographic coordinatesso that the NUGs may be used in determining the location of a mobiledevice exhibiting certain attributes as discussed in more detail below.

Accordingly, an embodiment of the present subject matter provides amethod for assigning geographical coordinates to a grid point located ina geographic region for the location of a mobile device where the methodprovides calibration data for each of one or more calibration points inthe geographic region and where for each of the calibration points theassociated calibration data is evaluated and based on that evaluation adetermination is made as to whether at least one grid point should bedefined, and if so, geographical coordinates are assigned to the gridpoint.

An additional embodiment of the present subject matter further includesin the above method a determination of geographical coordinates for eachof a plurality of nodes of a uniform grid spanning the geographic regionand for each of the grid points determining a closest node from theplurality of nodes and assigning characteristic data associated with thegrid point to the closest node.

A further embodiment includes a method of assigning geographicalcoordinates to a grid point located in a geographic region for thelocation of a mobile device where calibration data for each of one ormore calibration points in the geographic region are provided, and wherefor the calibration data associated with each of the calibration pointsthe calibration data is evaluated, a determination is made based on theevaluation as to whether at least one grid point should be defined, andgeographical coordinates are assigned to the grid point.

In another embodiment of the present subject matter, a system forassigning geographical coordinates to a grid point located in ageographic region is presented where the system includes a database anda processor for receiving calibration data for each of one or morecalibration points in the geographic region and for each of thecalibration points the processor is programmed to evaluate theassociated calibration data, determine if at least one grid point shouldbe defined based on the evaluation, assign geographical coordinates tothe at least one grid point, and populate the database with thegeographical coordinates.

A further embodiment of the present subject matter includes in the abovesystem circuitry for determining geographical coordinates for each of aplurality of nodes of a uniform grid spanning the geographic region, andcircuitry for determining, for each of the at least one grid point, aclosest node from the plurality of nodes and assigning characteristicdata associated with the grid point to the closest node.

Yet another embodiment of the present subject matter provides a methodof locating a mobile device. The method comprises the steps of providinga plurality of grid points in a geographic region, providing a pluralityof network measurement reports for a mobile device in the geographicregion, and comparing ones of the plurality of grid points to at leastone parameter of ones of the plurality of network measurement reports.The method further includes generating a first location estimate of themobile device for each of the ones of said plurality of networkmeasurement reports, and determining a second location estimate of themobile device as a function of at least one of the generated firstlocation estimates. An additional embodiment includes the step ofidentifying and omitting outlier first location estimates by determininga Mahalanobis distance from each first location estimate to the secondlocation estimate, determining a distance threshold from a median of theMahalanobis distances multiplied by a predetermined factor, anddetermining a third location estimate by averaging two or more of saidfirst location estimates. Another embodiment may also interpolatebetween ones of the plurality of grid points when more than one gridpoint corresponds to the at least one parameter of the plurality ofnetwork measurement reports. An additional embodiment may provide adefault location for the second location estimate if a second locationestimate cannot be determined as a function of at least one of thegenerated first location estimates.

An additional embodiment of the present subject matter provides a methodof improving a location estimate of a mobile device. The methodcomprises the steps of providing a plurality of grid points in ageographic region, providing a set of network measurement reports for amobile device in the geographic region, the set of network measurementreports including one or more subsets of network measurement reports,and comparing ones of the plurality of grid points to at least oneparameter of a subset of the network measurement reports. The methodfurther includes generating a first location estimate of the mobiledevice for each subset of network measurement reports, determining asecond location estimate of the mobile device as a function of at leastone of the generated first location estimates, and indicating anattribute of the second location estimate as a function of a parameterof a subset of the network measurement reports.

Another embodiment of the present subject matter provides a method oflocating a mobile device in a geographic region. The method comprisesthe steps of providing a plurality of grid points in a geographicregion, each of the grid points including at least one characterizingparameter and each of the grid points located on a grid defined over thegeographic region and providing a plurality of network measurementreports for a mobile device in the geographic region. The method alsocomprises determining an estimated location for the mobile device fromone network measurement report as a function of the at least oneparameter.

An additional embodiment of the present subject matter provides a methodof locating a mobile device in a geographic region. The method comprisesthe steps of providing a plurality of grid points in a geographicregion, each of the grid points including at least one characterizingparameter and each of the grid points located on a grid defined over thegeographic region and providing a plurality of network measurementreports for a mobile device in the geographic region. The method alsocomprises determining an estimated location for the mobile device from aset of said plurality of network measurement reports as a function ofthe parameter.

These embodiments and many other objects and advantages thereof will bereadily apparent to one skilled in the art to which the inventionpertains from a perusal of the claims, the appended drawings, and thefollowing detailed description of the embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart for a method for assigning geographicalcoordinates according to an embodiment of the present subject matter.

FIG. 2 is a flow chart for a method for assigning geographicalcoordinates including a calibration point according to an embodiment ofthe present subject matter.

FIG. 3 is a flow chart for a method for assigning geographicalcoordinates including calibration data according to an embodiment of thepresent subject matter.

FIG. 4 is a flow chart for a method for assigning geographicalcoordinates including clustering of data according to an embodiment ofthe present subject matter.

FIG. 5 is a flow chart for a method for assigning geographicalcoordinates including clustering of data vectors according to anembodiment of the present subject matter.

FIG. 6 is a flow chart for a method for assigning geographicalcoordinates including clustering according to an embodiment of thepresent subject matter.

FIG. 7 is a flow chart for a method for assigning geographicalcoordinates including determining outliers according to an embodiment ofthe present subject matter.

FIG. 8 is a flow chart for a method for assigning geographicalcoordinates including clustering of data vectors at the same calibrationpoint according to an embodiment of the present subject matter.

FIG. 9 is a flow chart for a method for assigning geographicalcoordinates including clustering of data vectors at the same calibrationpoint according to an embodiment of the present subject matter.

FIG. 10 is a flow chart for a method for assigning geographicalcoordinates to a grid point according to an embodiment of the presentsubject matter.

FIG. 11 is a flow chart for a method for assigning geographicalcoordinates including assigning geographical coordinates to a grid pointwhere only one calibration point is in a geographic region according toan embodiment of the present subject matter.

FIG. 12 is a flow chart for a method for assigning geographicalcoordinates including assigning geographical coordinates to a grid pointwhere there are plural calibration points in a geographic regionaccording to an embodiment of the present subject matter.

FIG. 13 is a flow chart for a method for assigning geographicalcoordinates including calibration data information according to anembodiment of the present subject matter.

FIG. 14 is a flow chart for a method for assigning geographicalcoordinates including evaluating calibration data according to anembodiment of the present subject matter.

FIG. 15 is a flow chart for a method for assigning geographicalcoordinates including populating a database with the geographicalcoordinates according to an embodiment of the present subject matter.

FIG. 16 is a flow chart for a method for assigning geographicalcoordinates including database information according to an embodiment ofthe present subject matter.

FIG. 17 is a flow chart for a method for assigning geographicalcoordinates including determining geographical coordinates for nodes ofa uniform grid according to an embodiment of the present subject matter.

FIG. 18 is a flow chart for a method for assigning geographicalcoordinates including characteristic data to nodes of uniform gridaccording to an embodiment of the present subject matter.

FIG. 19 is a flow chart for a method for assigning geographicalcoordinates for calibration data for each of one or more calibrationpoints in a geographic region according to an embodiment of the presentsubject matter.

FIG. 20 is a block diagram for a system for assigning geographicalcoordinates according to an embodiment of the present subject matter.

FIG. 21 is a block diagram for a system for assigning geographicalcoordinates including a determination of clustering of plural datavectors according to an embodiment of the present subject matter.

FIG. 22 is a block diagram for a system for assigning geographicalcoordinates including comparing clusters of data vectors from differentcalibration points according to an embodiment of the present subjectmatter.

FIG. 23 is a block diagram for a system for assigning geographicalcoordinates including comparing clusters of data vectors from the samecalibration point according to an embodiment of the present subjectmatter.

FIG. 24 is a block diagram for a system for assigning geographicalcoordinates including calibration data according to an embodiment of thepresent subject matter.

FIG. 25 is a block diagram for a system for assigning geographicalcoordinates including evaluating calibration data according to anembodiment of the present subject matter.

FIG. 26 is a block diagram for a system for assigning geographicalcoordinates including information for populating a database according toan embodiment of the present subject matter.

FIG. 27 is a block diagram for a system for assigning geographicalcoordinates including circuitry for determining geographical coordinatesfor nodes of a uniform grid according to an embodiment of the presentsubject matter.

FIG. 28 is a block diagram for a system for assigning geographicalcoordinates including characteristic data according to an embodiment ofthe present subject matter.

FIG. 29 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter.

FIG. 30 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingidentifying and omitting outlier first location estimates.

FIG. 31 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter.

FIG. 32 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingomitting a first location estimate.

FIG. 33 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includinginterpolating between grid points.

FIG. 34 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter includinginterpolating between grid points and/or assigning weights to selectedgrid points.

FIG. 35 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter includingproviding a default location.

FIG. 36 is a flow chart for a method of improving a location estimate ofa mobile device.

FIG. 37 is a flow chart for a method of improving a location estimate ofa mobile device according to another embodiment of the present subjectmatter.

FIG. 38 is a flow chart for a method of improving a location estimate ofa mobile device according to another embodiment of the present subjectmatter including omitting a first location estimate.

FIG. 39 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingidentifying and omitting outlier first location estimates.

FIG. 40 is a flow chart for a method of improving a location estimate ofa mobile device according to a further embodiment of the present subjectmatter.

FIG. 41 is a flow chart for a method of improving a location estimate ofa mobile device according to a further embodiment of the present subjectmatter including providing a default location.

FIGS. 42-55 are flow charts for methods of locating a mobile device in ageographic region according embodiments of the present subject matter.

DETAILED DESCRIPTION

With reference to the Figures where generally like elements have beengiven like numerical designations to facilitate an understanding of thepresent subject matter, the various embodiments of a system and methodfor generating a location estimate using non-uniform grid points areherein described.

The following description of the present subject matter is provided asan enabling teaching of the present subject matter and its best,currently-known embodiment. Those skilled in the art will recognize thatmany changes can be made to the embodiments described herein while stillobtaining the beneficial results of the present subject matter. It willalso be apparent that some of the desired benefits of the presentsubject matter can be obtained by selecting some of the features of thepresent subject matter without utilizing other features. Accordingly,those who work in the art will recognize that many modifications andadaptations of the present subject matter are possible and may even bedesirable in certain circumstances and are part of the present subjectmatter. Thus, the following description is provided as illustrative ofthe principles of the present subject matter and not in limitationthereof. While the following exemplary discussion of embodiments of thepresent subject matter may be directed primarily towards calibrationdata, it is to be understood that the discussion is not intended tolimit the scope of the present subject matter in any way and that theprinciples presented are equally applicable to other types of data,e.g., signal strength, GPS, NMR, Cell-ID, TDOA, RTT, TA, AOA, etc.,capable of being delivered by components in a communications networksuch as a base station, location measurement unit, other mobile devices,etc. In addition, the use of various combinations of all of thesesources, as in a hybrid location scheme, is within the scope of thesubject matter disclosed herein.

The present subject matter is directed generally to the problem ofestimating the location of a wireless mobile device using calibrationdata contained in one or more Network Measurement Reports (“NMRs”). Thecalibration data for various points must be gathered and analyzed sothat particular points (e.g., “grid points”) within the geographicregion can be determined and associated with a particular set or sets ofcalibration data from, for example, one or more NMRs. In order to do sogeographic coordinates may be assigned to grid points located in ageographic region. The grid points may be non-uniformly spacedthroughout the geographic region and hence may be referred to asnon-uniform grid points (“NUGs”). The location of a wireless mobiledevice may be estimated by comparing data reported by the mobile deviceto be geolocated with the data (and more particularly thecharacteristics derived from this data) associated with the various gridpoints to thereby estimate the location of the mobile.

The system and/or method of the present subject matter may apply to thesituation where calibration data is available over discrete points in a2-dimensional region “R” (3-D region is also contemplated such as withinlarge multi-level structures). The calibration data may be containedwithin a Network Measurement Report (“NMR”) as is known in the art orthe calibration data may be obtained using other known methods. Thecalibration data may be obtained at each of several calibration points,which may be discrete points within region R each having geographicalcoordinates (e.g., latitude and longitude) associated therewith. Thecalibration data may include, but is not limited to, the following: (a)signal strengths observed for signals transmitted by a set oftransmitters of known location within or in proximity to the region R;(b) signal strength of a transmitter located at the calibration point asmeasured by a set of receivers of known location within or in proximityto the region R; (c) round trip time for a signal between thecalibration point and an external known point; (d) time difference ofarrival at the calibration point with respect pairs of external pointslocated within or in proximity to region R as measured by either areceiver at the calibration point or the external points; (e) theserving cell or sector for a mobile wireless device operating at thatcalibration point; (f) the network state at the time of collection—afinite number of such states may be required to distinguish betweennetwork conditions that vary diurnally, weekly or in some other manner;and (g) combinations of the above.

As a non-limiting example, the case in (a) may apply to the IntegratedDigital Enhanced Network (“IDEN”) specification, (c) may apply to theGlobal System for Mobile communications (“GSM”) specification as in theTiming Advance (“TA”) parameter or the Round Trip Time (“RTT”) parameterin the Universal Mobile Telecommunications System (“UMTS”)specification, (d) may apply to the UMTS specification, while theexternal receivers may be the base stations. In general, the calibrationdata may be any of those measurements made by a mobile wireless devicelocated at the calibration point or any measurement made on thetransmissions or characteristics of the mobile wireless device at a setof external transmitter/receivers in the region R or in proximitythereto.

The calibration data may consist of many such sets (i.e., vectors)obtained at one or more calibration points. At each calibration point,the data gathering may have resulted in either a single data vector ormultiple data vectors, so that there are potentially multiple sets ofdata and/or data vectors associated with each calibration point.

A NUG generator or a method to produce NUGs may begin the NUG generationoperation using, for example, one of more of the following: (a) a fixeduniform grid (“UG”) defined over the region R with the calibration pointdata being assigned to the fixed grid points by some rule (e.g.,allocated by closest fixed grid point, a centroid of a set of fixed gridpoints, etc.); (b) random grid points to define the start of each NUG;(c) combinations of (a) and (b) depending on the characteristics of thecalibration data; or (d) some other useful method.

In any of these cases, the NUG generator may evaluate the data vectorsat a particular (candidate) calibration point, or at a fixed grid pointto which the data vector(s) is/are assigned. This calibration point orgrid point may serve as the root of a first NUG. The root of the NUG maybe the calibration data vector that initiates the creation of that NUG.The vectors may be examined using, for example, increasingly stringenttests of statistical sufficiency. In particular, a determination may bemade as to whether the data vectors exhibit clustering. If the dataexhibits tight clustering, the data for the next candidate calibrationpoint may be aggregated to the former calibration point and theclustering property may be re-evaluated. For example, if the secondcalibration point also has a cluster but this cluster is sufficientlydifferent than the cluster of the first calibration point, adetermination may be made that the data for the two consideredcalibration points should be allocated to the roots of separate NUGs. Ifthe aggregate cluster (i.e., a cluster including data from both thefirst and second calibration points) is statistically very similar toeither of the first or second clusters (taken independently), then thedata for the two calibration points may be allocated to the same NUG.All adjacent calibration data points may be similarly evaluated withrespect to the first considered calibration point. Thus one or more ofthe adjacent calibration points may either wind up having all their dataaccumulated into a single NUG or, at the other extreme, each suchcalibration point may become the root of a separate NUG.

The primary test made to determine the allocation may be one of avariety of clustering tests, such as, for example, the K-meansalgorithm. Statistical similarity may be determined by, for example, theprobability density function (“pdf”) of the data parameters (e.g.,neighboring cell signal levels, timing information, etc.), the mean andvariance of the data parameters, the serving cell/sector, or otherfunctions of the calibration data.

Those measurements or parameter values that do not cluster may bereferred to as outliers. The performance of a grid-based patternmatching system such as that disclosed herein is typically dependent onstored received signal level measurements that accurately reflect thelevels that are likely to be reported by the mobile device to belocated. If the drive test data, for example, used to create the RFsignal level grid contains outlier measurements, the statisticallyconsistent value of the signal level will be distorted. Therefore, thepresent subject matter also describes a system and method used toidentify and eliminate outlier signal level measurements and timingadvance values (or in general, any parameter within the NMR) during NUGor grid creation so as to improve the estimate of the mean parametervalue.

As a non-limiting example, in a very simple consideration of clusteringone could consider the mean value of a parameter. In this scenario,neighbor cell control channel signal level measurement outliers could beeliminated as follows: At each grid point, the average received signallevel of a particular control channel signal may be computed from all ofthe measurements of that signal assigned to the grid point. Thedeviation of each individual measurement from the mean may be computed.Measurements that deviate by more than a configurable predeterminedthreshold from the mean may be omitted. The average may then berecomputed without the omitted outliers. In a scenario where there arevery few measurements, typically less than five or so, the original meanvalue will be greatly influenced by any outlier measurements and thusmay falsely identify too many of the measurements as outliers, or failto detect outliers at all. For this reason, another parameter is used toonly perform the outlier check if there are at least a minimum number ofmeasurements.

In a more general case, a cluster may be a region in N-dimensional NMRvector space where there is a sufficient number of such vectors with amutual variation such that the mutual variation could be ascribed purelyto noise in the measurement. Thus, for example, if within a few feet ofthe original measurement, if a particular parameter is blocked (say by alarge structure such as a building) that parameter would fall out of theoriginal cluster. If sufficient such blocked locations have data fallingnear the original cluster, one may obtain a secondary cluster where thedifference between the first and second clusters is the large variationin this particular parameter.

In addition, if any of the examined sets of data associated with acalibration point exhibit more than one cluster, it may be necessary todefine one or more co-located NUGs. Thus, if there are, for example,three well defined clusters associated with a particular calibrationpoint, these clusters could form the roots of three co-located NUGs. Thedata in these NUGs may grow depending on whether similar clusters canalso be found in adjacent (or close) calibration points in which casethe similar clusters may be aggregated to the original NUGs or, if theadjacent clusters are not similar, the adjacent clusters (or cluster)may form separate root NUGs (or NUG).

Further, if the quantity of data associated with a particularcalibration point is insufficient to sensibly test for statisticalsimilarity or clustering, data from adjacent calibration grid points maybe accumulated first and the statistical or clustering test performedthereafter. Thus, based on the results of the clustering test using theaccumulated data the determination of how one should separate out thedata into NUGs may be made.

The technique may be repeated until all calibration grid points in theregion R are exhausted. At the end of this process one has divided theregion into a collection of NUGs, where multiple co-located NUGs mayexist. The NUGs may fully cover the region R and each NUG may havestatistically similar data accumulated into itself. The geometricalshape (i.e., the shape defined by the union of locations of calibrationpoints assigned to the NUG) and the amount of data accumulated into suchNUGs is seen to be variable since these are determined by thestatistical similarity of the data allocated to a NUG.

Additionally, we may also consider the method of generating NUGs basednot on statistical consistency of calibration data, but on otherconditions such as (a) a minimum number of unique neighbors observed indata accumulated from allocated calibration grid points; (b) a minimumnumber of data vectors (NMRs); (c) a maximum and/or minimum NUG radius;(d) a specific set of neighboring cells; (e) a specific set ofneighboring cells with power ordering; or (f) any combination of theabove. Additionally, the method of using statistical consistency orsimilarity or data clustering combined with any of these otherconditions may be employed.

For each so obtained NUG, a variety of parameters and functions may begenerated and stored to describe that NUG. These are termed the NUGcharacteristics. The NUG characteristics are a representation in thatattempt to capture the nature and variability of the data associatedwith that NUG in a compact and representative form. Thesecharacteristics may include, but are not limited to, the following: (a)an ordered list of neighboring cells; (b) functions defined on theabsolute neighboring cell power levels (e.g., mean, median, k^(th)moment, cluster-mean, etc.); (c) functions defined on the relativeneighboring cell power differences (e.g., mean, median, k^(th) moment,cluster-mean, etc.); (d) serving cell/sector; (e) timing advanceparameter (or equivalent); (f) individual pdf (probability densityfunction or probability distribution function) of each neighboring cellpower level; (g) joint pdf of neighboring cell power levels; (h) meanand variance of neighboring cell power levels; (i) mobile deviceorientation (e.g., indoors, outdoors, direction mobile device is facing(e.g., North, South, etc.), tilted upwards, azimuth, elevation, etc.);(j) a compact and/or efficient representation that enables retrieval ofthe calibration data NMR vectors assigned to this NUG; (k) the networkstate as indicated in the calibration data; (l) a confidence measureindicative of the reliability of the calibration data feeding this NUG;and (m) any combinations of the above.

If a pdf is determined for a NUG, that pdf may be generated using eitherthe Parzen technique or the method of Gaussian mixtures or some variantthereof. In addition when a need to specify the variance or covarianceexists, that parameter may be set to a value dependent on the observedvariance for a particular neighboring cell power level or the observedcovariance matrix for a set of neighboring cell power levels.

The location ascribed to the NUG may be, for example, any internal pointwithin the NUG. If the NUG contains only a single calibration point, thelocation of the NUG may be set as the location of the calibration point.If the NUG encompasses several calibration points, the location of anyone of the calibration points or the centroid of such calibration pointsor some other similar measure may be used to define the NUG location.Also, in the case of multiple co-located NUGs, all such NUGs may havetheir assigned location set to the same value.

With reference now to FIG. 1, a flow chart is depicted for a method forassigning geographical coordinates according to an embodiment of thepresent subject matter. At block 101, calibration data may be providedfor each of one or more calibration points in a geographic region. Atblock 102, for each of the calibration points calibration dataassociated with the calibration point is evaluated and a determinationis made as to whether a grid point, such as a NUG, should be defined. Ifit is determined that a grid point is to be defined, geographicalcoordinates are assigned to the grid point so that the grid point may beuseful in estimating the location of a mobile device.

FIG. 2 is a flow chart for a method for assigning geographicalcoordinates including a calibration point according to an embodiment ofthe present subject matter. Blocks 201 and 202 are similar to blocks 101and 102, respectively. At block 213, the calibration point may belocated on a predetermined fixed uniform grid defined over thegeographic region or the calibration point may be randomly locatedwithin the geographic region.

FIG. 3 is a flow chart for a method for assigning geographicalcoordinates including calibration data according to an embodiment of thepresent subject matter. Blocks 301 and 302 are similar to blocks 101 and102, respectively. At block 313, the calibration data associated withone or more calibration points may be comprised of information from aNMR, or the calibration data for a particular calibration point may beobtained from one or more mobile devices located at or in closeproximity to the calibration point, or the calibration data for aparticular calibration point may be obtained from a signal transmittedfrom a mobile device (or devices) located at or in close proximity tothe calibration point where the signal is received by a receiver in orin proximity to the geographic region.

FIG. 4 is a flow chart for a method for assigning geographicalcoordinates including clustering of data according to an embodiment ofthe present subject matter. Blocks 401 and 402 are similar to blocks 101and 102, respectively. At block 413, for one or more of the calibrationpoints the calibration data may include multiple data vectors and, atblock 414, the evaluation of the data vectors may include adetermination of clustering of the multiple data vectors as describedabove.

Considering now the flow chart depicted in FIG. 5, the flow chartindicates a method for assigning geographical coordinates includingclustering of data vectors according to an embodiment of the presentsubject matter. Blocks 501 and 502 are similar to blocks 101 and 102,respectively. At block 503, the determination of whether at least onegrid point should be defined based on the evaluation of the calibrationdata associated with a calibration point includes a comparison of afirst cluster of data vectors from a first calibration point to a secondcluster of data vectors where the second cluster of data vectorsincludes the first cluster of data vectors as well as data vectors froma second calibration point. At block 504, if the comparison in block 503results in the difference between the first and second cluster of datavectors being within a predetermined tolerance value, then the datavectors from the first and second calibration points are assigned to thesame grid point. However, if the comparison is not within tolerance,then the data vectors from the first calibration point are assigned to afirst grid point and the data vectors from the second calibration pointare assigned to a second grid point.

The flow chart shown in FIG. 6 illustrates another method for assigninggeographical coordinates including clustering according to an embodimentof the present subject matter. Here, blocks 601, 602, 603, and 604 aresimilar to blocks 501, 502, 503, and 504, respectively. At block 615 theevaluation of calibration data for one or more calibration points mayinclude determining the clustering of plural data vectors using aK-means analysis. At block 616 the comparing of clusters of data vectorsmay include determining a probability density function of an aspect ofthe data vectors.

FIG. 7 is a flow chart for a method for assigning geographicalcoordinates including determining outliers according to an embodiment ofthe present subject matter. Blocks 701, 702, 713, and 714 are similar toblocks 401, 402, 413, and 414, respectively. At block 703, adetermination of outlier data vectors may be made and the outlier datavectors may be eliminated from the determination of data vectorclustering.

Regarding FIG. 8, a flow chart is represented for a method for assigninggeographical coordinates including clustering of data vectors at thesame calibration point according to an embodiment of the present subjectmatter. Blocks 801 and 802 are similar to blocks 101 and 102,respectively. At block 803, the determination if at least one grid pointshould be defined based on the evaluation of calibration data mayinclude a comparison of a first cluster of data vectors associated witha first calibration point to a second cluster of data vectors associatedwith the first calibration point. If the result of the comparison iswithin a predetermined tolerance, then the data vectors from the firstand second clusters may be assigned to the same grid point; otherwise,the data vectors from the first cluster may be assigned to a first gridpoint while the data vectors from the second cluster may be assigned toa second grid point.

FIG. 9 is a flow chart illustrating another method for assigninggeographical coordinates including clustering of data vectors at thesame calibration point according to an embodiment of the present subjectmatter. Here, blocks 901, 902, 903, and 904 are similar to blocks 801,802, 803, and 804, respectively. At block 915 the geographicalcoordinates assigned to the first and second grid points may beidentical.

Directing attention now towards FIG. 10, a flow chart is presented for amethod for assigning geographical coordinates to a grid point accordingto an embodiment of the present subject matter. Blocks 1001 and 1002 aresimilar to blocks 101 and 102, respectively. At block 1013, thegeographical coordinates assigned to a first grid point may be differentthan the geographical coordinates assigned to a second grid point or thegeographical coordinates assigned to a first grid point may be the sameas the geographical coordinates assigned to a second grid point.

FIG. 11 is a flow chart for a method for assigning geographicalcoordinates including assigning geographical coordinates to a grid pointwhere only one calibration point is in a geographic region according toan embodiment of the present subject matter. Blocks 1101 and 1102 aresimilar to blocks 101 and 102, respectively. At block 1113, if there isonly one calibration point within the geographic region, then thegeographical coordinates assigned to a grid point may result in the gridpoint being located within a predetermined radius of the one calibrationpoint. Or, the geographical coordinates assigned to a grid point may beidentical to the geographical coordinates of the calibration point.

Moving now to FIG. 12, a flow chart is shown for a method for assigninggeographical coordinates including assigning geographical coordinates toa grid point where there are plural calibration points in a geographicregion according to an embodiment of the present subject matter. Blocks1201 and 1202 are similar to blocks 101 and 102, respectively. At block1213, where there are multiple calibration points in the geographicregion, the geographical coordinates assigned to a grid point may resultin the grid point being located within a predetermined radius of acentroid of a polygon formed by connecting the multiple calibrationpoints.

FIG. 13 is a flow chart for a method for assigning geographicalcoordinates including calibration data information according to anembodiment of the present subject matter. Blocks 1301 and 1302 aresimilar to blocks 101 and 102, respectively. At block 1313, thecalibration data may include one or more of the following: signalstrength for a signal transmitted by a transmitter having a knownlocation as received by a receiver at a calibration point; signalstrength of a signal transmitted by a transmitter located at acalibration point as received by a receiver at a known location; roundtrip time for a signal traveling between a calibration point and a knownlocation; timing advance of a signal received by a mobile device at acalibration point; time difference of arrival of plural signals at acalibration point with respect to a pair of known locations as measuredby a receiver at a calibration point or at the known locations; theidentification of a serving cell or serving sector of a mobile devicelocated at a calibration point; a state of a wireless network serving amobile device, and combinations thereof.

FIG. 14 is a flow chart for a method for assigning geographicalcoordinates including evaluating calibration data according to anembodiment of the present subject matter. Blocks 1401 and 1402 aresimilar to blocks 101 and 102, respectively. At block 1413, theevaluating of the calibration data associated with a calibration pointmay include an evaluation such as: a minimum number of uniqueneighboring calibration points as determined by calibration data of theneighboring calibration points; a minimum number of data vectors ornetwork measurement reports; a predetermined maximum or minimum radiusfrom a calibration point; a predetermined set of cells neighboring acell serving a mobile device; and combinations thereof.

FIG. 15 is a flow chart for a method for assigning geographicalcoordinates including populating a database with the geographicalcoordinates according to an embodiment of the present subject matter.Blocks 1501 and 1502 are similar to blocks 101 and 102, respectively. Atblock 1503, a database may be populated with the geographicalcoordinates assigned to the grid points.

FIG. 16 is a flow chart for a method for assigning geographicalcoordinates including database information according to an embodiment ofthe present subject matter. Blocks 1601, 1602, and 1603 are similar toblocks 1501, 1502, and 1503, respectively. At block 1604, the databasemay be populated with information such as: a list of cells neighboring acell serving a mobile device; a quantity that is a function of a powerlevel of one or more cells neighboring a cell serving a mobile device;an identity of a cell or a sector serving a mobile device; a timingadvance parameter; a geographical orientation of a mobile device; alocation of a mobile device; network measurement report data vectors; astate of a network serving a mobile device; a confidence measureindicative of a reliability of the calibration data; and combinationsthereof.

Directing attention now to FIG. 17, a flow chart is presented for amethod for assigning geographical coordinates including determininggeographical coordinates for nodes of a uniform grid according to anembodiment of the present subject matter. Blocks 1701 and 1702 aresimilar to blocks 101 and 102, respectively. At block 1703, geographicalcoordinates may be determined for the nodes of a uniform grid spanningthe geographic region. At block 1704, for each of the grid points, adetermination of the closest node of the uniform grid is made and thecharacteristic data associated with the grid point may be assigned tothe closest node.

Further, FIG. 18 is a flow chart for a method for assigning geographicalcoordinates including characteristic data to nodes of uniform gridaccording to an embodiment of the present subject matter. Here, blocks1801, 1802, 1803, and 1804 are similar to blocks 1701, 1702, 1703, and1704, respectively. At block 1805, the characteristic data may include alist of cells neighboring a cell serving a mobile device; a quantitythat is a function of a power level of one or more cells neighboring acell serving a mobile device; an identity of a cell or a sector servinga mobile device; a timing advance parameter; a geographical orientationof a mobile device; a location of a mobile device; network measurementreport data vectors; a state of a network serving a mobile device; aconfidence measure indicative of a reliability of the calibration data;and combinations thereof.

With reference to FIG. 19, a flow chart is illustrated for a method forassigning geographical coordinates for calibration data for each of oneor more calibration points in a geographic region according to anembodiment of the present subject matter. At block 1901, calibrationdata may be provided for each of one or more calibration points in ageographic region. At block 1902, for the calibration data for each ofthe calibration points in the geographic region, the calibration data isevaluated and a determination is made as to whether a grid point shouldbe defined based on the evaluation. If it is determined that a gridpoint is to be defined, geographical coordinates are assigned to thegrid point so that the grid point may be useful in estimating thelocation of a mobile device.

With attention now directed to FIG. 20, a block diagram is presentedthat represents a system for assigning geographical coordinatesaccording to an embodiment of the present subject matter. A database2001 is operatively connected to a processor 2002. The processor 2002 iscapable of receiving calibration data for each of one or morecalibration points in a geographic region. The processor 2002 may beprogrammed, as shown in block 2003, to evaluate the calibration dataassociated with the calibration points, determine if at least one gridpoint should be defined based on the evaluation, assign geographicalcoordinates to the one or more grid points, and populate the database2001 with the geographical coordinates.

FIG. 21 is a block diagram for a system for assigning geographicalcoordinates including a determination of clustering of plural datavectors according to an embodiment of the present subject matter. Thedatabase 2101, the processor 2102, and block 2103 are similar to thedatabase 2001, the processor 2002, and block 2003, as described above,respectfully. At block 2114, for each of select ones of the calibrationpoints, the calibration data may include multiple data vectors and theevaluating of the calibration data may include a determination ofclustering of the multiple data vectors.

FIG. 22 is a block diagram for a system for assigning geographicalcoordinates including comparing clusters of data vectors from differentcalibration points according to an embodiment of the present subjectmatter. The database 2201, the processor 2202, block 2203, and block2214 are similar to the database 2101, the processor 2102, block 2103,and block 2114, as described above, respectfully. At block 2215, thedetermination if at least one grid point should be defined based on theevaluation may include comparing a first cluster of data vectors from afirst one of the select calibration points to a second cluster of datavectors, where the second cluster of data vectors may include the firstcluster of data vectors and data vectors from a second one of the selectcalibration points. At block 2216, if the result of the comparison iswithin a predetermined tolerance, then the data vectors from the firstand second calibration points may be assigned to the same grid point;otherwise, the data vectors from the first calibration point may beassigned to a first grid point and the data vectors from the secondcalibration point may be assigned to a second grid point.

FIG. 23 is a block diagram for a system for assigning geographicalcoordinates including comparing clusters of data vectors from the samecalibration point according to an embodiment of the present subjectmatter. The database 2301, the processor 2302, block 2303, and block2314 are similar to the database 2101, the processor 2102, block 2103,and block 2114, as described above, respectfully. At block 2315, thedetermination if at least one grid point should be defined based on theevaluation may include comparing a first cluster of data vectors from afirst one of the select calibration points to a second cluster of datavectors from the first one of the select calibration points. At block2316, if the result of the comparison is within a predeterminedtolerance, then the data vectors from the first and second calibrationpoints may be assigned to the same grid point; otherwise, the datavectors from the first cluster may be assigned to a first grid point andthe data vectors from the second cluster may be assigned to a secondgrid point.

Looking now at FIG. 24, a block diagram is presented representing asystem for assigning geographical coordinates including calibration dataaccording to an embodiment of the present subject matter. The database2401, the processor 2402, and block 2403 are similar to the database2001, the processor 2002, and block 2003, as described above,respectfully. At block 2414, the calibration data may include: signalstrength for a signal transmitted by a transmitter having a knownlocation as received by a receiver at a calibration point; signalstrength of a signal transmitted by a transmitter located at acalibration point as received by a receiver at a known location; roundtrip time for a signal traveling between a calibration point and a knownlocation; timing advance of a signal received by a mobile device at acalibration point; time difference of arrival of multiple signals at acalibration point with respect to a pair of known locations as measuredby a receiver at a calibration point or at the known locations; theidentification of a serving cell or serving sector of a mobile devicelocated at a calibration point; a state of a wireless network serving amobile device, and combinations thereof.

FIG. 25 is a block diagram for a system for assigning geographicalcoordinates including evaluating calibration data according to anembodiment of the present subject matter. The database 2501, theprocessor 2502, and block 2503 are similar to the database 2001, theprocessor 2002, and block 2003, as described above, respectfully. Atblock 2514, the evaluation of the associated calibration data mayinclude an evaluation such as: a minimum number of unique neighboringcalibration points as determined by calibration data of the neighboringcalibration points; a minimum number of data vectors or networkmeasurement reports; a predetermined maximum or minimum radius from acalibration point; a predetermined set of cells neighboring a cellserving a mobile device; and combinations thereof.

FIG. 26 is a block diagram for a system for assigning geographicalcoordinates including information for populating a database according toan embodiment of the present subject matter. The database 2601 and theprocessor 2602 are similar to the database 2001 and the processor 2002,as described above, respectfully. At block 2603, the processor 2602 maybe programmed to evaluate the calibration data associated with thecalibration points, determine if at least one grid point should bedefined based on the evaluation, assign geographical coordinates to theone or more grid points, populate the database 2601 with thegeographical coordinates, and populate the database 2601 withinformation which may include: a list of cells neighboring a cellserving a mobile device; a quantity that is a function of a power levelof one or more cells neighboring a cell serving a mobile device; anidentity of a cell or a sector serving a mobile device; a timing advanceparameter; a geographical orientation of a mobile device; a location ofa mobile device; network measurement report data vectors; a state of anetwork serving a mobile device; a confidence measure indicative of areliability of the calibration data; and combinations thereof.

FIG. 27 is a block diagram for a system for assigning geographicalcoordinates including circuitry for determining geographical coordinatesfor nodes of a uniform grid according to an embodiment of the presentsubject matter. The database 2701, the processor 2702, and block 2703are similar to the database 2601, the processor 2602, and block 2603, asdescribed above, respectfully. The system may further comprise circuitry2704 for determining geographical coordinates for each of a plurality ofnodes of a uniform grid spanning the geographic region, and circuitry2705 for determining, for each of the one or more grid points, a closestnode from the plurality of nodes of the uniform grid and assigningcharacteristic data associated with each of the grid point to itsclosest node.

FIG. 28 is a block diagram for a system for assigning geographicalcoordinates including characteristic data according to an embodiment ofthe present subject matter. The database 2801, the processor 2802, block2803, circuitry 2804, and circuitry 2805 are similar to the database2701, the processor 2702, block 2703, circuitry 2704, and circuitry2705, as described above, respectfully. At block 2816, thecharacteristic data may include: a list of cells neighboring a cellserving a mobile device; a quantity that is a function of a power levelof one or more cells neighboring a cell serving a mobile device; anidentity of a cell or a sector serving a mobile device; a timing advanceparameter; a geographical orientation of a mobile device; a location ofa mobile device; network measurement report data vectors; a state of anetwork serving a mobile device; a confidence measure indicative of areliability of the calibration data; and combinations thereof.

In a typical signal strength pattern matching mobile location system thetime allowed to produce a location may be such that multiple NMRs andsets and subsets thereof may be available. By way of a non-limitingexample, in the GSM air standard, mobile measurements are reported at anapproximate rate of approximately two per second. Generally, the timeallowed to produce a location may be on the order of thirty seconds. Itis therefore an aspect of embodiments of the present subject matter toimprove location accuracy by combining individual locations fromcalibration data, e.g., multiple NMRs, to produce a final locationestimate.

Grid-based signal strength pattern matching location systems typicallydetermine a quantitative measure of how close each candidate grid pointmatches with mobile-reported measurement parameters. An estimate of amobile device's location may then be given by a grid point having theclosest match thereto or a location interpolated between several gridpoint locations. As multiple NMRs are generally available during thetime allotted to report the estimated location of a mobile device,embodiments of the present subject matter may utilize each NMR togenerate an independent location estimate. These independent orindividual location estimates may then be averaged or anothermathematical function employed thereon to produce a final estimatedmobile location that may be statistically more accurate.

Many location systems may also “fall back” to a default location as afunction of the serving cell when the system is unable to determine agrid point match location. In such an instance, a location statusvariable may be utilized to identify the default location as a fall backlocation. Such a fall back location is generally less accurate than alocation estimate determined by a pattern matching location system;however, an exemplary location combiner may omit any fall back locationsand average or combine location estimates determined by an exemplarypattern matching algorithm.

A correlation may exist between location accuracy and mismatch distancemetrics, e.g., “cost” values. The correlation may be exploited byflagging individual location estimates as having a high cost or metric(e.g., using a location status variable), and the corresponding locationestimates are likely to possess a large location error. Embodiments ofthe present subject matter may present a refinement to the locationcombiner by skipping or omitting individual locations exceeding apredetermined “cost” threshold. Thus, the resulting location accuracymay be significantly improved. In further embodiments, the correlationbetween mismatch distance metrics and location accuracy may be exploitedby employing weighted averaging of the individual estimated locations,weighting by an inverse of the metrics, normalizing by a sum of theinverses, or any combination thereof. A further metric that may beutilized for weighting the contribution of individual location estimatesto a final location estimate may be the number of reporting neighboringcells. By way of a non-limiting example, assuming five individuallocation estimates are combined and four of the five individual locationestimates possessed six reporting neighboring cells and the fifthlocation estimate possessed four reporting neighboring cells, the fifthlocation estimate may then be de-weighted in the final locationestimation.

Another embodiment of the present subject matter may identify and omitoutlier individual location estimates to improve the final locationestimation. For example, a Mahalanobis distance from each individuallocation estimate to the final location estimate may be determined. Adynamic distance threshold may be determined from the median of thesedistances multiplied by a configurable factor. An individual locationestimate having a distance to the final location estimate exceeding thethreshold may be identified as an outlier. The final location estimatemay then be re-determined with the outlier locations omitted. In theevent that weighted averaging is utilized in such a determination, theweights may be re-determined prior to the final location estimation.

It may also be noted that estimated locations derived utilizing subsetsof available NMRs may often differ. For example, considering an NMRincluding a set of ordered (e.g., in descending magnitude) reportingneighboring cell (NC) power levels, with the NC having an order ABCDE.If the lowest power NC (NC=E) is omitted from the NMR, the locationsdetermined using ABCD NCs may be different from ABCDE. Similarly, thelocation determined for ABCE NCs may differ from that for ABDE NCs.

An examination or evaluation of the location estimates derived fromsubsets of the NMRs may provide an indication regarding the quality ofthe final location estimate. By way of a non-limiting example, if thelocation estimates derived utilizing any possible method of mapping theNMR or sets thereof to a specific geographic location or grid point,whether using NUGs or uniform grid points, agree under combinations ofsubsets, the confidence in the location estimate may be high and thusrepresent a confidence measure on the location estimate. Further, thefraction of total location estimates within a predetermined distance ofthe final location estimate may also qualify as a confidence measure.

In one embodiment of the present subject matter, for each NMR, one mayform a set of all subsets of a selected NMR. Therefore, in anon-limiting example of an ordered set of NCs given by ABC, a full setof subsets is {ABC, AB, AC, BC, A, B, C}. In each case, an estimatedlocation may be derived utilizing any method of location. Each of thelocations in this set of locations, L, may possess an associatedprobability or other measure derived from the particular locationmethod, thus defining a set M. A variety of schemes may be defined andimplemented upon the set L and the set of associated measures on L,given by M, such as, but not limited to: (a) computing the finalestimated location by clustering the set L without any reference to themeasures in M; (b) computing the final estimated location as thecentroid of a region containing the tightest cluster in L having anaggregate measure higher than some pre-set value; (c) computing thefinal estimated location as the location of the NUG (e.g., centroid ofthe NUG) which occurs most often in L; (d) computing the final estimatedlocation by clustering the subset of L obtained by dropping the leastpower member in the NMR successively (e.g., the subset {ABC, AB, A});(e) computing the final estimated location as the subset of L obtainedby successively dropping the least power member in the NMR and withweighting by the corresponding measure in M.

Considering that the individual marginal probabilities for each NMRcomponent, characteristic or parameter over a set of candidate NUGs oruniform grid points (UG) have been determined, it may be assumed thatfor every subset in L, the measure set M provides the joint probabilityfor the subsets of the NMR. Using the subset AB in the previous example,the marginal probability for A, B and C over all NUGs has beendetermined. To determine the joint probability of A and B, for example,the marginal probabilities for A and B may be multiplied over the NUGs(or other locations). This generates the measure set M, and having theset L and set M defined, any one or combination of the methods in(a)-(e) described in the previous paragraph may be applied thereto foran estimation of an exemplary confidence measure.

The same principles may be applied to multiple NMRs and each of theirrespective subsets where each subset of each NMR may be assigned itsrespective measure in a now larger set M. It follows that the methods in(a)-(e) described above are equally applicable. In the case of multipleNMRs, a representative NMR may be determined through a clusteringalgorithm applied to each parameter of the NMR viewed over the set ofNMRs. The methods in (a)-(e) described above may then be applied to thisrepresentative NMR for an estimation of an exemplary confidence measure.

It is also an aspect of embodiments of the present subject matter toprovide an estimate of the location error in a signal strength patternmatching location system. As discussed above, a confidence measure maybe determined that provides an indication of the quality of the locationestimate.

In one embodiment of the present subject matter, if the final estimatedlocation is an average of the individual locations, the degree to whichthe individual locations are clustered around the final estimation mayprovide an indication of the location error. The error estimate may bedetermined as the average of the distances from each individual locationto the final estimated location as a function of the followingrelationship:

$\begin{matrix}{\hat{e} \equiv \frac{\sum\limits_{i = 1}^{N}d_{i}}{N}} & (1)\end{matrix}$

where N is the number of estimated locations and d_(i) is the Euclideandistance from the i^(th) estimated location to the final estimatedlocation.

The error estimate may also be determined as a function of the followingrelationship:

$\begin{matrix}{\hat{e} \equiv \frac{\sum\limits_{i = 1}^{N}{w_{i}d_{i}}}{\sum\limits_{i = 1}^{N}w_{i}}} & (2)\end{matrix}$

where N is the number of estimated locations, d_(i) is the Euclideandistance from the i^(th) estimated location to the final estimatedlocation, and w_(i) is a series of weighting factors.

As discussed above, when subsets of available NMRs are considered,however, the estimated locations may also differ. Therefore, anexemplary confidence measure may also be defined upon an estimatedlocation given by any function that increases as the number of subsetlocations agree with the final estimated location. A non-limitingexample of such a function may be the fraction of total locations thatagree with the final estimated location or the fraction of totallocations that lie within a certain distance of the final estimatedlocation. In a further embodiment, weights may be assigned to thelocation estimates by utilizing the parameters or functions employed indetermining the estimated location to thereby weight the determinationof the associated confidence measure. Further exemplary confidencemeasures may be a function of pdfs, distortion measures, Mahalanobisdistances, etc. with respect to any one or sets of NUGs.

Exemplary weighting quantities, e.g., distortion measures, pdfs, etc.,may also be derived while estimating any location from single andmultiple NMRs or their subsets, and may also be utilized to estimatelocation error. Empirically, the magnitudes of these weightingquantities may be correlated with the expected error. This relationshipmay be established graphically or in tabular format as a function ofenvironmental characteristics (e.g., urban, suburban, seasonal, etc.).As a result, given a set of weighting quantities, an associated errormay be predicted for a specific location estimate.

In one embodiment of the present subject matter, if the set of derivedlocations utilizing a set and/or subset of NMRs exhibit clusters,cluster separation may be employed between the highest aggregateweighted clusters to define an expected error. Such a distance may betermed as an inverse confidence measure as the larger the distancebecomes, the greater the chance of error in the final location estimateif the corresponding cluster were selected. It follows that if theaggregate weight for a distant cluster is small, this distance should bemodified to de-weight the associated distance by the weight of thecluster. An exemplary determination may multiply the cluster distance bya ratio of the weight of a selected cluster to the weight of a distantcluster; however, many such variations of this fundamental idea areclearly conceivable and such an example should not limit the scope ofthe claims appended herewith.

In another embodiment of the present subject matter, when each of theindividual location estimates are generally at the same location (e.g.,each located at the same calibration or grid point) the resulting errorestimate would be zero or near zero. In such a scenario, the errorestimate may be bounded by a minimum error value such as, but notlimited to, a configurable constant based upon the overall expectedsystem accuracy (e.g., the 25th percentile of overall system error,etc.).

It should be noted that the statistical averaged or weighted averagedlocation accuracy improves as the number of individual locationestimates averaged or determined increases. For example, a finallocation estimate that comprises the average of two individual locationsmay generally be less accurate than a final location estimate comprisingan average of twenty individual location estimates. Further, the optimalnumber of location estimates to combine or consider is dependent uponseveral factors including, but not limited to, the speed of the mobiledevice, the rate of acquiring NMRs, etc. This relationship may also beutilized to improve the error estimate as the number of individuallocation estimates increases.

In embodiments of the present subject matter wherein any one or multipleindividual location estimates are “fall back” locations (e.g., defaultlocations that may be based upon serving cell identification location),a default error estimate may be determined based upon an expectedstatistical accuracy of cell identification location. This determinationmay be a function of cell site geometry in an associated orcorresponding operating market and may also be determined empiricallythrough accuracy testing. Exemplary scenarios in which default locationsmay be encountered include, but are not limited to, when the NMR doesnot contain any NC measurements, when the available set of NMRs for themobile device location generates a set of candidate locations that doesnot cluster (e.g., when the individual location estimates appear to berandomly distributed over a geographic region), when an NMR has very fewreporting NCs and the confidence measure is poor, and combinationsthereof.

In embodiments of the present subject matter where NMR data may bemissing or invalid, the coordinates of the cell serving a mobile devicemay be retrieved from a respective site database from the serving cellidentifier. In this instance, an exemplary default location may be alocation that is a configurable distance away from the serving site. Theconfigurable distance may or may not be positioned at a heading alongthe serving sector azimuth. For air standards in which certainparameters (e.g., timing advance, round trip timing, etc.) areavailable, this data may also be converted to an approximate rangeestimate from the serving site and utilized with other applicableparameters. For example, when such parameters are available, the defaultlocation may be enhanced by selecting a location on the serving cellazimuth at a distance from the site given by a TA range estimate.

In embodiments of the present subject matter where an NMR may includeTime Difference of Arrival (“TDOA”) data, this parameter may be utilizedto derive a region within the cell to constrain the default location.For example, the TDOA, assuming the base station time offsets are known,defines a hyperbola in the region of interest. An intersection of thishyperbola with the applicable TA region to this cell may be utilized asa default location estimate. Alternatively, a default location estimatemay be employed that does not rely on a serving sector heading if thereexists apriori knowledge of sector coverage density. For example, if asector coverage region can be determined (e.g., through drive testing,etc.), then the centroid of the sector coverage region may be stored inthe respective site database by sector for each site and retrieved as adefault location.

A further aspect of embodiments of the present subject matter may alsoimprove location accuracy by interpolating between grid point locationswhen more than one grid point matches the calibration or reported datawithin a predetermined value. Generally, grid-based signal strengthpattern matching location systems determine a quantitative measure ofhow close each candidate grid point (e.g., NUG or UG) matches mobiledevice reported measurement parameters. The location estimate of themobile device may be given by the grid point having a match within apredetermined range. Further, as the actual location of the mobiledevice is generally not constrained to lie at a grid point location,interpolation between grid points may result in a more accurate locationestimate.

During an exemplary interpolation according to one embodiment of thepresent subject matter, an analysis of whether interpolation should beperformed may be determined as well as a selection of the appropriategrid or calibration points for the interpolation. Distance metrics mayalso be determined on any number of grid points. Exemplary metrics arediscussed above and may include, but are not limited to, pdfs,Mahalanobis distance between parameter vectors, ordering number betweenordered NCs in the NMR, NUG, UG, and combinations thereof. By way of anon-limiting example, it may be assumed that the distance metric foreach of N candidate grid points (N>1) is determined and sorted.Representing the distance metric as C, then for each i^(th) candidate,i=1 . . . N, an appropriate metric may be determined by the followingrelationship:

C_Ratio_(i) [C _(i) −C _(min) ]/[C _(N) −C _(min)]  (3)

where C_(i) is a metric associated with an i^(th) candidate grid point,C_(N) is a metric associated with the worst corresponding candidate gridpoint, and C_(min) is a metric associated with the best correspondingcandidate grid point. It follows that grid points having a C_(ratio)less than a predetermined and configurable threshold value may be acandidate for interpolation.

Generally, interpolation occurs between adjacent or nearby grid points.To minimize or prevent interpolation across widely spaced grid points,the distance from each interpolation candidate grid point to the minimumcost grid point may be less than a configurable distance threshold.

In embodiments when there are few grid point candidates or when thereare fewer than a configurable number of candidate grid points, anappropriate metric may be determined by the following relationship:

C_Ratio_(i) =[C _(i) −C _(min) ]/[C _(min)]  (4)

where C_(i) is a metric associated with an i^(th) grid point and C_(min)is a metric associated with the best corresponding grid point. Equation(4) may thus enable an identification of appropriate grid points forinterpolation when N is small. Equation (4) may also be performed toprevent interpolation between widely separated grid points.

Once the grid points for interpolation have been identified, oneembodiment of the present subject matter may employ weighted averagingto determine a final interpolated location. An exemplary weight assignedto the i^(th) grid point in computing the final interpolated locationmay be determined by the following relationship:

$\begin{matrix}{{Wi} = \frac{\frac{1}{Ci}}{\sum\limits_{i = 1}^{N}\left( \frac{1}{Ci} \right)}} & (5)\end{matrix}$

where C_(i) is a metric associated with an i^(th) grid point. Weightedaveraging may also be utilized rather than uniform weighting to ensurethat the best matching grid point (i.e., minimum cost grid point) exertsa larger influence on the final location estimate.

As discussed above, each grid point (NUG or UG) may provide one or aplurality of parameters and/or functions characterizing the grid point.Given a received set of one or more NMRs obtained at an unknownlocation, an accurate estimation of the unknown location may bedetermined using a characterization of the grid points over a geographicregion. In one embodiment of the present subject matter, a distortionmeasure may be determined between available NMRs and grid pointcharacteristics to assist in the estimation.

Generally, embodiments of the present subject matter may utilize anynumber of methods to determine a distortion measure, e.g., a mismatchdistance between mobile reported measurements and a candidate gridpoint's stored measurement data. The associated “cost” value may also beinversely proportional to an increasing function of the probability thatthe mobile device is located at or in the vicinity of a grid point.

In one embodiment of the present subject matter, a distortion measuremay comprise a combination of the values of each parameter in an NMR andeach corresponding parameter in the grid point (NUG or UG)characteristics. The distortion measure may generally increase as themismatch between any of the parameters increases and vice versa. Forexample, an exemplary cost value may be determined utilizing aMahalanobis distance provided by the following relationship:

$\begin{matrix}{{COST} = {{\alpha \left( {{TA}_{rpt} - {TA}_{cond}} \right)}^{2} + {\sum\limits_{i}\left\lbrack {\left( \frac{{{RxLevDiff}(i)}^{2}}{{MAX}\; {DIFF}^{2}} \right){NCCU}} \right\rbrack}}} & (6)\end{matrix}$

where α is 0 or 1 which controls whether TA differences are included inthe determination (e.g., 1 for GSM and 0 for iDEN), TA_(rpt) is the TAfor the NMR, TA_(cand) is the TA for the candidate grid point in thecalibration database and/or a representative value, RxLevDiff(i)represents the difference in RxLev (received signal strength) for thei^(th) neighbor cell or serving cell, I is an index to neighbor orserving cells (e.g., if only NC received signal strengths are used andthere are six reporting NCs, then i=1 to 6; if in addition, a servingcell signal strength is included then i=1 to 7), NCCU represents an NCcost unit where an increasing NCCU increases the cost penalty for RxLevdifference relative to the cost penalty for TA difference, and MAXDIFFis a configurable parameter that limits the cost incurred fordifferences in signal strengths. MAXDIFF may be set to 20 dB or anotherconfigurable value. RxLev differences exceeding MAXDIFF would not incuran additional cost penalty.

Embodiments of the present subject matter may determine RxLevDiff(i)through any number of methods including, but not limited to, thefollowing relationships:

RxLevDiff(i)=(RxLevServ—RxLevNeigh(i))_(NMR)−(RxLevServ—RxLevNeigh(i))_(CND)  (7)

for i=1:6;

RxLevDiff(i)=(RxLevNeigh(i))_(NMR)−(RxLevNeigh(i))CND  (8)

for i=1:6;

RxLevDiff(i)=(RxLevNeigh(1)−RxLevNeigh(i))NMR−(RxLevNeigh(1)−RxLevNeigh(i))_(CND)  (9)

for i=2:6;

RxLevDiff(i)=(AvgRxLevNeigh−RxLevNeigh(i))NMR−(AvgRxLevNeigh−RxLevNeigh(i))_(CND)  (10)

for i=1:6;

RxLevDiff(i)=(AvgRxLev−RxLev(i))_(NMR)−(AvgRxLev−RxLev(i))_(CND)  (11)

for i=1:7.

With reference to the above relationships, Equation (7) provides acomparison between the signal strengths of the serving cell and thei^(th) NC between NMR and candidate points, Equation (8) provides acomparison between the signal strengths of the i^(th) NC between NMR andcandidate points, Equation (9) provides a comparison between the signalstrengths of a first common NC and the i^(th) NC between NMR andcandidate points, Equation (10) provides a comparison between theaverage signal strengths of the NCs and the signal strengths of the itNC between NMR and candidate points, and Equation (11) provides acomparison of the average signal strengths of the serving cells and NCsand the signal strengths of the i^(th) serving cell and NC between NMRand candidate points.

In a further aspect of embodiments of the present subject matter, anestimated location may be generated for a mobile device given a receivedNMR and a region “R” over which a set “S” of grid points (NUGs or UGs)have been established. As described above, each grid point may include aseries of parameters, components and/or functions characterizing therespective grid point. Provided a received set of one or more NMRsobtained at some unknown location, an estimation of that unknownlocation may be determined as a function of a characterization of thegrid points over this region R.

In one embodiment of the present subject matter, an estimated locationof a mobile device may be determined using any single NMR (drawn from aset or subset of NMRs) by any number of the following methods orcombinations thereof. For example, one embodiment may match an orderedlist of NCs, where the ordering may be in terms of any one of a numberof parameters characterizing the NMR, such as, for example, NC powerlevel, in a respective NMR to a similarly ordered list of NCs in thegrid point(s) (NUG or UG) and (a) generate the estimated location as thecentroid of the best cluster of matching grid points, (b) generate theestimated location as the location of the highest joint probabilitymatching grid point, (c) generate the estimated location as the (jointprobability) weighted sum of the locations of a set of matching gridpoints, (d) generate the estimated location as the (joint probability)weighted sum of the clustered locations of a set of matching grid points(i.e., cluster the locations of the matching grid points and then applya cumulative probability for all contained grid points in a cluster asweight for the respective cluster), (e) generate the estimated locationby combining locations derived from NMR subsets, or any combinationthereof.

In an additional embodiment, if the ordered list of NCs in the NMR(ordered with respect to the magnitude of any particular parametercharacterizing the NMR) does not provide an exact match to the orderedlist of NCs that may be available as a characteristic of a grid point,the method may evaluate those grid points (NUGs or UGs) including anordered list thereof that may contain an ordered list in the NMR. Thus,the ordered list in the NMR may form a subset of the ordered list in thegrid point, and the method may (a) generate an estimated location as thecentroid of the best cluster of matching grid points, (b) generate theestimated location as the location of the highest joint probabilitymatching grid point, (c) generate the estimated location as the (jointprobability) weighted sum of the locations of a set of matching gridpoints, (d) generate the estimated location as the (joint probability)weighted sum of the clustered locations of a set of matching gridpoints, (e) generate the estimated location by combining locationsderived from NMR subsets, or any combination thereof.

In embodiments of the present subject matter where the ordered list ofNCs in the NMR is not contained in the ordered list of NCs for any gridpoint, then the method may utilize the highest power subset of NCs inthe NMR (e.g., ordered from highest to lowest power) that provideseither an exact match or is contained in the ordered list of NCs in thegrid point. The method may then (a) generate an estimated location asthe centroid of the best cluster of matching grid points, (b) generatethe estimated location as the location of the highest joint probabilitymatching grid point, (c) generate the estimated location as the (jointprobability) weighted sum of the locations of a set of matching gridpoints, (d) generate the estimated location as the (joint probability)weighted sum of the clustered locations of a set of matching gridpoints, (e) generate the estimated location by combining locationsderived from NMR subsets, or any combination thereof.

In a further embodiment of the present subject matter, the individualpdf of each NC power level or other parameter in the NMR over the set ofavailable grid points (NUGs or UGs) may be evaluated. A jointprobability may then be determined as the product of such marginalprobabilities and an estimated location generated as (a) the location ofthe highest joint probability matching grid point, (b) the (jointprobability) weighted sum of the locations of a set of matching gridpoints, (c) the (joint probability) weighted sum of the clusteredlocations of a set of matching grid points, (d) a combination oflocations derived from NMR subsets, or any combination thereof.

In one embodiment, the joint probability of the NMR NC power levels orother parameters may be evaluated over the set of available grid points.The method may then generate an estimated location as (a) the locationof the highest joint probability matching grid point, (b) the (jointprobability) weighted sum of the locations of a set of matching gridpoints, (c) the (joint probability) weighted sum of the clusteredlocations of a set of matching grid points, (d) a combination oflocations derived from NMR subsets, or any combination thereof.

In another embodiment, a distortion measure may be determined betweenthe grid point measured parameters (e.g., mean NC power level, TA value,RTT value, etc.) and corresponding parameters in the NMR. Exemplarydistortion measures have been provided above and may be, but are notlimited to, a Mahalanobis distance, etc. Any weighting in this distanceover dissimilar parameters may also be optimized empirically ordetermined by calculation. Utilizing an exemplary distortion measure,the method may generate an estimated location as (a) the location of thegrid point with a least distortion measure, (b) the weighted sum of thelocations of the set of grid points (weights may also be applied as afunction of the distortion measure), (c) the weighted sum of any one ormultiple clustered locations of a set of grid points, (d) a combinationof locations derived from NMR subsets (where the measure set M is thedistortion measure).

In yet another embodiment, an estimated location may be selected bymatching the NC power level or other parameter ordering in the NMR tothe NC power or other parameter ordering in the grid points (NUG or UG)and utilize an ordering number evaluation. An exemplary ordering numbermay be an indicator regarding the number of relative shifts in positionoccurring between the NMR and the grid point(s) NC ordered power levelsor other parameters. By way of a non-limiting example, in an NMR the NCpower levels may be ordered as ABCDE whereas the selected grid point mayprovide an ordering of BACDE. This results in an ordering number of 1 (asingle shift). Multiple variations of the ordering number may beconsidered and determined, however, these variations may be a measure ofthe distortion in ordering between the NC power levels evident in thegrid point as compared to the NC parameters seen in the NMR. Anaggregate ordering number for a set of NMRs may then be obtained bycombining the ordering numbers for each individual NMR. A finalestimated location may be a grid point cluster having the smallestordering number. In a further embodiment, the ordering number orordering of an exemplary characteristic may also be regarded as adistortion measure such that the method may generate an estimatedlocation as (a) the location of the grid point with a least distortionmeasure, (b) the weighted sum of the locations of the set of grid points(weights may also be applied as a function of the distortion measure),(c) the weighted sum of any one or multiple clustered locations of a setof grid points, (d) a combination of locations derived from NMR subsets(where the measure set M is the distortion measure).

With respect to any of the various methods described above, embodimentsof the present subject matter may also filter parameters prior togenerating an estimated location. For example, an embodiment may matchthe serving cell/sector of candidate grid points to the servingcell/sector of the NMR and then proceed with any of the methodspreviously described. A further embodiment may also filter candidategrid points using other available parameters (e.g., TA, RTT, etc.) forthe NMR and then proceed with any of the methods previously described.This exemplary filtering may also be set wider than the actual parameter(TA, RTT, etc.) determined value by a configurable threshold.

Candidate grid points may also be filtered by utilizing a mobileorientation parameter (e.g., indoors, outdoors, facing North or South,titled upwards, azimuth and elevation, etc.) applied during constructionof the grid points, by utilizing those grid points where the servingcell identifier and serving control channel agrees with the NMR data, byutilizing the magnitude of the serving cell forward link control channelreceived signal level (and applying a configurable tolerance to thisparameter), by eliminating those grid points not having at least N(configurable) NC received signal levels in common with the NMR, or anycombination thereof and then proceeding with any of the methodspreviously described.

In embodiments of the present subject matter where a set of NMRsobtained at some unknown location (or locations in close proximity toeach other) may be available, the aforementioned methods may be expandedto exploit the multiplicity of information. For example, an estimatedlocation may be generated for each NMR in a set of NMRs by any or all ofthe methods applicable to a single NMR as indicated above and a tightestcluster of a single NMR location may be determined. Such a determinationmay also utilize a metric derived while locating that NMR (e.g., jointprobability, Mahalanobis distance, etc.) to weight the clusters.

In another example, a representative NMR may be generated from a set ofNMRs and any or all of the methods applicable to a single NMR asindicated above may be applied thereto. The representative NMR may begenerated by obtaining a representative value for each of the NCs seenin the respective set of NMRs, and similarly obtaining a representativevalue for each of the other parameters seen in the NMRs. In anotherembodiment, these representative values may be obtained as a function ofthe available set of values, e.g., for NC power level mean or medianpower may be utilized.

In a further example, transitions in any of the parameters within theset of NMRs may be observed. Transitions may be utilized in reducing theapplicable region for an estimated location. As many parametersrepresent a range of location possibilities, when a parameter changes,it may be inferred that the region of interest is at a boundary of theranges represented by the parameter prior to and after the change.Therefore, any one of these boundaries determined for any parameterchange within the applicable set may reduce the candidate region for themobile device location. Exemplary parameter changes may be, but are notlimited to, changes in signal power levels with respect to a particularNC, rate or pattern of dropping in and out of a particular NC signal,changes in serving cell or sector, changes in TA, RTT or equivalentparameter, and combinations thereof.

Additional embodiments may determine a distortion measure between thegrid point characteristics or measured parameters and correspondingparameters of all NMRs taken collectively without reduction torepresentative values. Any weighting in this measure over dissimilarparameters may be optimized empirically. Utilizing a distortion measure,exemplary methods may generate an estimated location as (a) the locationof the grid point having the least distortion measure, (b) the weightedsum of the locations of a set of grid points (any weights applied mayalso be a function of the distortion measure), (c) the weighted sum ofclustered locations of a set of grid points, (d) as a combination oflocations derived from NMR subsets (where the measure set M is now thedistortion measure).

Further embodiments may match a variety of parameters and functionsgenerated and stored describing a grid point (i.e., the grid pointcharacteristics) with similar parameters and functions determined forthe set of NMRs by utilizing a distortion measure to evaluate thesimilarity therebetween. Exemplary parameters and functions may be, butare not limited to, the following: (a) an ordered list of neighboringcells; (b) functions defined on the absolute neighboring cell powerlevels (e.g., mean, median, k^(th) moment, cluster-mean, etc.); (c)functions defined on the relative neighboring cell power differences(e.g., mean, median, k^(th) moment, cluster-mean, etc.); (d) servingcell/sector; (e) timing advance parameter (or equivalent); (f)individual pdf (probability density function or probability distributionfunction) of each neighboring cell power level; (g) joint pdf ofneighboring cell power levels; (h) mean and variance of neighboring cellpower levels; (i) mobile device orientation (e.g., indoors, outdoors,direction mobile device is facing (e.g., North, South, etc.), tiltedupwards, azimuth, elevation, etc.); (j) a compact and/or efficientrepresentation that enables retrieval of the calibration data NMRvectors assigned to this grid point; (k) the network state as indicatedin the calibration data; (l) a confidence measure indicative of thereliability of the calibration data feeding this grid point; and (m) anycombinations of the above. In this exemplary method, further embodimentsmay also construct a pdf for the set of NMRs and determine a similarityto the pdf for a grid point by a measure applicable to pdfs such as, butnot limited to, the Bhattacharya distance, Kullback-Liebler divergenceor other measures for probability distributions. Having generated suchmeasures with respect to the set of candidate grid points the method maygenerate an estimated location as (a) the location of the grid pointwith the least distortion measure, (b) the weighted sum of the locationsof a set of grid points (any weights applied as a function of thedistortion measure), (c) the weighted sum of clustered locations of aset of grid points, (d) a combination of locations derived from NMRsubsets (where the measure set M is the distortion measure).

FIG. 29 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter. Withreference to FIG. 29, at block 2910, a plurality of calibration or gridpoints may be provided in a geographic region. At block 2920, aplurality of NMRs may be provided for a mobile device in the geographicregion. Ones of the plurality of grid points may be compared to one ormore parameters of ones of the plurality of NMRs in block 2930. Inanother embodiment of the present subject matter, the comparison mayfurther comprise generating a distortion measure that is a function of acombination of parameters of ones of the plurality of networkmeasurement reports and corresponding parameters of ones of theplurality of grid points. An exemplary distortion measure may be, but isnot limited to, a Mahalanobis distance, a comparison of received signalstrengths of a serving cell and a neighboring cell between ones of theplurality of NMRs and grid points, a comparison of received signalstrengths of a neighboring cell between ones of the plurality of NMRsand grid points, a comparison of received signal strengths of a firstcommon neighboring cell and another neighboring cell between ones of theplurality of NMRs and grid points, a comparison of average receivedsignal strengths of reporting neighboring cells and received signalstrengths of a neighboring cell between ones of the plurality of NMRsand grid points, a comparison of average received signal strengths ofserving and reporting neighbor cells received signal strengths of aselected neighbor or serving cell between ones of the plurality of NMRsand grid points, and any combination thereof. A first location estimateof the mobile device may be generated for each of the ones of theplurality of NMRs in block 2940, and in block 2950, a second locationestimate of the mobile device may be determined as a function of atleast one of the generated first location estimates.

FIG. 30 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingidentifying and omitting outlier first location estimates. Withreference to FIG. 30, blocks 3010, 3020, 3030, 3040 and 3050 are similarto blocks 2910, 2920, 2930, 2940 and 2950, respectively. At block 3060,the method may further determine a Mahalanobis distance from each firstlocation estimate to the second location estimate and at block 3070,determine a distance threshold from a median of the determinedMahalanobis distances multiplied by a predetermined factor. At block3080, a third location estimate may then be determined by averaging twoor more of the first location estimates. In such a determination thefirst location estimates having a Mahalanobis distance to the secondlocation estimate greater than a predetermined distance threshold may beomitted from the third location estimate determination.

FIG. 31 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter. Withreference to FIG. 31, blocks 3110, 3120, 3130, 3140 and 3150 are similarto blocks 2910, 2920, 2930, 2940 and 2950, respectively. At block 3151,the determination of a second location estimate may further compriseaveraging two or more first location estimates, or at block 3152, thedetermination of a second location estimate may further compriseemploying a weighted averaging of ones of the first location estimates.At block 3153, the determination of a second location estimate mayfurther comprise weighting a first location estimate by an inverse of adistance metric, or at block 3154, the determination of a secondlocation estimate may further comprise normalizing a first locationestimate by a sum of an inverse of a distance metric. Further, at block3155, the determination of a second location estimate may furthercomprise weighting a first location estimate as a function of the numberof reporting neighboring cells to a serving cell serving the mobiledevice.

FIG. 32 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingomitting a first location estimate. With reference to FIG. 32, blocks3210, 3220, 3230, 3240 and 3250 are similar to blocks 2910, 2920, 2930,2940 and 2950, respectively. At block 3260, the method may further omita first location estimate having an error greater than a predeterminedthreshold.

FIG. 33 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includinginterpolating between grid points. With reference to FIG. 33, blocks3310, 3320, 3330, 3340 and 3350 are similar to blocks 2910, 2920, 2930,2940 and 2950, respectively. At block 3360, the method may furtherinterpolate between ones of the plurality of grid points when more thanone grid point corresponds to a parameter of the plurality of NMRs.

FIG. 34 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter includinginterpolating between grid points and/or assigning weights to selectedgrid points. With reference to FIG. 34, blocks 3410, 3420, 3430, 3440,3450 and 3460 are similar to blocks 3310, 3320, 3330, 3340, 3350 and3360, respectively. At block 3461, the interpolation may be a functionof the relationship provided in Equation (3) above. With reference toEquation (3), a grid point having a C_Ratio_(i) less than apredetermined threshold may be a candidate grid point for theinterpolation in one embodiment and/or a grid point having a distancefrom the best corresponding grid point less than a predeterminedthreshold may be a candidate grid point for the interpolation. At block3462, the interpolation may also be a function of the relationshipprovided in Equation (4) above. At block 3470, the method may furtherassign weights to an i^(th) grid point as a function of the relationshipprovided in Equation (5) above.

FIG. 35 is a flow chart for a method for locating a mobile deviceaccording to another embodiment of the present subject matter includingproviding a default location. With reference to FIG. 35, blocks 3510,3520, 3530, 3540, and 3550 are similar to blocks 2910, 2920, 2930, 2940and 2950, respectively. At block 3560, the method may further compriseproviding a default location for the second location estimate if asecond location estimate cannot be determined as a function of at leastone of the generated first location estimates. In one embodiment atblock 3561, the default location may be a predetermined distance from aserving cell serving the mobile device at a heading along a servingsector azimuth. In another embodiment at block 3562, the defaultlocation may be a function of timing advance or round trip time and/ormay be an approximate range estimate from a serving cell serving themobile device. In an additional embodiment in block 3563, the defaultlocation may be a region determined as a function of a TDOA measurementand/or where the region is the intersection of a hyperbola defined bysaid TDOA with a timing advance region applicable to a serving cellserving the mobile device. In yet a further embodiment in block 3564,the default location may be determined as a function of a prioriknowledge of sector coverage density.

FIG. 36 is a flow chart for a method of improving a location estimate ofa mobile device. With reference to FIG. 36, at block 3610, a pluralityof grid points in a geographic region may be provided and at block 3620,a set of NMRs for a mobile device in the geographic region may beprovided. The set of NMRs may or may not include one or more subsets ofNMRs. At block 3630, ones of the plurality of grid points may becompared to at least one parameter of a subset of the NMRs, and at block3640, a first location estimate of the mobile device may be generatedfor each subset of NMRs. A second location estimate of the mobile devicemay be determined as a function of at least one of the generated firstlocation estimates at block 3650, and at block 3660, an attribute of thesecond location estimate may be indicated as a function of a parameterof a subset of the NMRs. In additional embodiments, the attribute may bedetermined as a function of any one or combination of a fraction offirst location estimates corresponding with the second location estimateand a fraction of total first location estimates that lie within apredetermined distance of the second location estimate.

FIG. 37 is a flow chart for a method of improving a location estimate ofa mobile device according to another embodiment of the present subjectmatter. With reference to FIG. 37, blocks 3710, 3720, 3730, 3740, 3750and 3760 are similar to blocks 3610, 3620, 3630, 3640, 3650 and 3660,respectively. At block 3751, the determination of a second locationestimate may further comprise clustering the set of NMRs withoutreference to parameters in any of the subsets of NMRs. At block 3752,the second location estimate may be determined as the centroid of aregion containing the tightest cluster of NMRs in the set of NMRs. Thecluster may or may not possess an aggregate measure higher than apredetermined threshold. At block 3753, the second location estimate maybe determined as the centroid of ones of the plurality of grid pointsoccurring most often in the set of NMRs. At block 3754, thedetermination of a second location estimate may further compriseclustering a subset of NMRs as a function of power of neighboring cells.At block 3755, the determination of a second location estimate mayfurther comprise clustering a subset of NMRs as a function of power ofneighboring cells and another parameter in the subset. At block 3756,the determination of a second location estimate may further compriseaveraging two or more first location estimates. At block 3757, thedetermination of a second location estimate may also further compriseemploying a weighted averaging of ones of the first location estimates.In another embodiment, at block 3758, the determination of a secondlocation estimate may further comprise weighting a first locationestimate by an inverse of a distance metric. At block 3759, thedetermination of a second location estimate may also comprisenormalizing a first location estimate by a sum of an inverse of adistance metric, and at block 3761, the determination of a secondlocation estimate may further comprise weighting a first locationestimate as a function of the number of reporting neighboring cells to aserving cell serving the mobile device. In another embodiment at block3762, the determination of a second location estimate may furthercomprise weighting a first location estimate as a function of theindicated attribute. Exemplary weighting may be, but is not limited to,a Mahalanobis distance, a probability density function.

FIG. 38 is a flow chart for a method of improving a location estimate ofa mobile device according to another embodiment of the present subjectmatter including omitting a first location estimate. With reference toFIG. 38, blocks 3810, 3820, 3830, 3840, 3850 and 3860 are similar toblocks 3610, 3620, 3630, 3640, 3650 and 3660, respectively. At block3870, the method may further omit a first location estimate having anerror greater than a predetermined threshold.

FIG. 39 is a flow chart for a method for locating a mobile deviceaccording to one embodiment of the present subject matter includingidentifying and omitting outlier first location estimates. Withreference to FIG. 39, blocks 3910, 3920, 3930, 3940, 3950 and 3960 aresimilar to blocks 3610, 3620, 3630, 3640, 3650 and 3660 respectively. Atblock 3970, the method may further determine a Mahalanobis distance fromeach first location estimate to the second location estimate and atblock 3980, determine a distance threshold from a median of thedetermined Mahalanobis distances multiplied by a predetermined factor.At block 3990, a third location estimate may then be determined byaveraging two or more of the first location estimates. In such adetermination the first location estimates having a Mahalanobis distanceto the second location estimate greater than a predetermined distancethreshold may be omitted from the third location estimate determination.

FIG. 40 is a flow chart for a method of improving a location estimate ofa mobile device according to a further embodiment of the present subjectmatter. With reference to FIG. 40, blocks 4010, 4020, 4030, 4040, 4050and 4060 are similar to blocks 3610, 3620, 3630, 3640, 3650 and 3660,respectively. At block 4061, the indication of an attribute of thesecond location estimate may further comprise determining an errorestimate as an average of distances from each first location estimate tothe second location estimate. In one embodiment at block 4062, the errorestimate may be determined as a function of the relationship provided inEquation (1) above. In a further embodiment at block 4063, the errorestimate may be determined as a function of the relationship provided inEquation (2) above. With reference to Equation (2), exemplary weightingfactors may be, but are not limited to probabilities determined duringthe first location estimate generation, probabilities determined duringthe second location estimate determination, distortion function valuesdetermined during the first location estimate generation, distortionfunction values determined during the second location estimatedetermination, and combinations thereof. In yet another embodiment inblock 4064, the error estimate may be determined as a function of subsetNMR cluster separation between a highest aggregate weighted cluster.

FIG. 41 is a flow chart for a method of improving a location estimate ofa mobile device according to a further embodiment of the present subjectmatter including providing a default location. With reference to FIG.41, blocks 4110, 4120, 4130, 4140, 4150 and 4160 are similar to blocks3610, 3620, 3630, 3640, 3650 and 3660, respectively. At block 4170, adefault location may be provided for the second location estimate if theattribute is less than a predetermined threshold. In one embodiment atblock 4171, the default location may be a predetermined distance from aserving cell serving the mobile device at a heading along a servingsector azimuth. In another embodiment at block 4172, the defaultlocation may be a function of timing advance or round trip time and/ormay be an approximate range estimate from a serving cell serving themobile device. In an additional embodiment in block 4173, the defaultlocation may be a region determined as a function of a TDOA measurementand/or where the region is the intersection of a hyperbola defined bysaid TDOA with a timing advance region applicable to a serving cellserving the mobile device. In yet a further embodiment in block 4174,the default location may be determined as a function of a prioriknowledge of sector coverage density.

FIG. 42 is a flow chart for a method of locating a mobile device in ageographic region according to an embodiment of the present subjectmatter. With reference to FIG. 42, at block 4210, a plurality of gridpoints may be provided in a geographic region where each of the gridpoints may include at least one characterizing parameter orcharacterizing function, and where each of the grid points is located ona grid defined over the geographic region. At block 4220, a plurality ofNMRs may be provided for a mobile device in the geographic region, andat block 4230, an estimated location may be determined for the mobiledevice from one NMR as a function of the at least one parameter. Ofcourse, one or more of the grid points may be randomly located withinthe geographic region, and one or more of the grid points may be locatedon a predetermined fixed uniform grid defined over the geographicregion.

FIG. 43 is a flow chart for a method of locating a mobile device in ageographic region according to another embodiment of the present subjectmatter. With reference to FIG. 43, blocks 4310, 4320 and 4330 aresimilar to blocks 4210, 4220 and 4230, respectively. At block 4340, thedetermination of an estimated location for the mobile device may furtherinclude comparing an ordered list of cells neighboring a cell servingthe mobile device in the one NMR to an ordered list of neighboring cellsin the grid. The ordering may be in terms of any one of a number ofparameters characterizing a respective NMR, e.g., NC power level or NCmeasurement quality. At block 4350, an estimated location may begenerated for the mobile device which may comprise a centroid of acluster of best matching grid points in the grid, a highest jointprobability matching grid point in the grid, a weighted sum of thelocations of a set of matching grid points in the grid (exemplaryweights may be defined by any number of means such as, but not limitedto, a joint probability derived from individual pdfs, etc.), a weightedsum of clustered locations of a set of matching grid points in the grid(i.e., cluster the locations of the matching grid points and then applya cumulative probability for all contained grid points in a cluster as aweight for the respective cluster), as a function of locationsdetermined from subsets of the plurality of NMRs, or any combinationthereof. At block 4360, a further embodiment may also filter availablegrid points as a function of any one or combination of selectedcharacteristics, such as, but not limited to, a matching of serving cellor sector for grid points in the grid to the serving cell (provided sucha characterization is available for the respective grid points) orsector of the one NMR, a TA parameter from the one NMR, a RTT parameterfrom the one NMR, a mobile device orientation parameter, a serving cellidentifier, a serving control channel, a magnitude of a serving cellforward link control channel received signal level, a predeterminednumber of cell received signal levels common to the one NMR, or anycombination thereof.

FIG. 44 is a flow chart for a method of locating a mobile device in ageographic region according to an additional embodiment of the presentsubject matter. With reference to FIG. 44, blocks 4410, 4420 and 4430are similar to blocks 4210, 4220 and 4230, respectively. At block 4440,the determination of an estimated location for the mobile device mayfurther include comparing an ordered list of cells neighboring a cellserving the mobile device in the one NMR to a similarly ordered list ofneighboring cells in the grid. The ordering may be in terms of any oneof a number of parameters characterizing a respective NMR, e.g., NCpower level. At block 4450, if no exact match exists between the orderedlist of neighboring cells of the one NMR and any grid point on the grid,then a largest subset of the ordered list of neighboring cells in thegrid points may be formed that matches the NMR. At block 4460, anestimated location may be generated for the mobile device which maycomprise a centroid of a cluster of thus matched grid points, clusteredby location, in the grid, a highest joint probability matching gridpoint in the grid, a weighted sum of the locations of a set of matchinggrid points in the grid (exemplary weights may be defined by any numberof means such as, but not limited to, a joint probability derived fromindividual pdfs, etc.), a weighted sum of clustered locations of a setof matching grid points in the grid (i.e., cluster the locations of thematching grid points and then apply a cumulative probability for allcontained grid points in a cluster as a weight for the respectivecluster), as a function of locations determined from subsets of theplurality of NMRs, or any combination thereof. At block 4470, a furtherembodiment may also filter available grid points as a function of anyone or combination of selected characteristics, such as, but not limitedto, a matching of serving cell or sector for grid points (provided sucha characterization is available for those grid points) in the grid tothe serving cell or sector of the one NMR, a TA parameter from the oneNMR, a RTT parameter from the one NMR, a mobile device orientationparameter, a serving cell identifier, a serving control channel, amagnitude of a serving cell forward link control channel received signallevel, a predetermined number of cell received signal levels common tothe one NMR, or any combination thereof.

FIG. 45 is a flow chart for a method of locating a mobile device in ageographic region according to a further embodiment of the presentsubject matter. With reference to FIG. 45, blocks 4510, 4520 and 4530are similar to blocks 4210, 4220 and 4230, respectively. At block 4540,the determination of an estimated location for the mobile device mayfurther include comparing an ordered list of cells neighboring a cellserving the mobile device in the one NMR to an ordered list ofneighboring cells in the grid. The ordering may be in terms of any oneof a number of parameters characterizing a respective NMR, e.g., NCpower level. At block 4550, if the ordered list of neighboring cells ofthe one NMR is not contained in the ordered list of neighboring cellsfor the grid, then a largest ordered subset of neighboring cells in theone NMR may be utilized having either an exact match or is contained inthe ordered list of neighboring cells in the grid. At block 4560, anestimated location of the mobile device may be generated that maycomprise a centroid of a cluster, clustered by location, of thus matchedgrid points in the grid, a highest joint probability matching grid pointin the grid, a weighted sum of the locations of a set of matching gridpoints in the grid (exemplary weights may be defined by any number ofmeans such as, but not limited to, a joint probability derived fromindividual pdfs, etc.), a weighted sum of clustered locations of a setof matching grid points in the grid (i.e., cluster the locations of thematching grid points and then apply a cumulative probability for allcontained grid points in a cluster as a weight for the respectivecluster), and as a function of locations determined from subsets of theplurality of NMRs. At block 4570, a further embodiment may also filteravailable grid points as a function of any one or combination ofselected characteristics, such as, but not limited to, a matching ofserving cell or sector for grid points (provided such a characterizationis available for those grid points) in the grid to the serving cell orsector of the one NMR, a TA parameter from the one NMR, a RTT parameterfrom the one NMR, a mobile device orientation parameter, a serving cellidentifier, a serving control channel, a magnitude of a serving cellforward link control channel received signal level, a predeterminednumber of cell received signal levels common to the one NMR, or anycombination thereof.

FIG. 46 is a flow chart for a method of locating a mobile device in ageographic region according to yet another embodiment of the presentsubject matter. With reference to FIG. 46, blocks 4610, 4620 and 4630are similar to blocks 4210, 4220 and 4230, respectively. At block 4640,the determination of an estimated location for the mobile device mayfurther include evaluating a probability density function for each powerlevel of a cell neighboring a cell serving the mobile device in the oneNMR over each grid point of a set of available grid points in the grid.At block 4650, a joint probability may then be determined as a functionof the individual probability density functions, and at block 4660, anestimated location of the mobile device may be generated that maycomprise a centroid of a cluster of highest probability grid points,clustered by location, in the grid, a highest joint probability matchinggrid point in the grid, a weighted sum of the locations of a set ofmatching grid points in the grid (exemplary weights may be defined byany number of means such as, but not limited to, a joint probabilityderived from individual pdfs, etc.), a weighted sum of clusteredlocations of a set of matching grid points in the grid (i.e., clusterthe locations of the matching grid points and then apply a cumulativeprobability for all contained grid points in a cluster as a weight forthe respective cluster), and as a function of locations determined fromsubsets of the plurality of NMRs. At block 4670, a further embodimentmay also filter available grid points as a function of any one orcombination of selected characteristics, such as, but not limited to, amatching of serving cell or sector for grid points (provided such acharacterization is available for the respective grid points) in thegrid to the serving cell or sector of the one NMR, a TA parameter fromthe one NMR, a RTT parameter from the one NMR, a mobile deviceorientation parameter, a serving cell identifier, a serving controlchannel, a magnitude of a serving cell forward link control channelreceived signal level, a predetermined number of cell received signallevels common to the one NMR, or any combination thereof.

FIG. 47 is a flow chart for a method of locating a mobile device in ageographic region according to yet another embodiment of the presentsubject matter. With reference to FIG. 47, blocks 4710, 4720 and 4730are similar to blocks 4210, 4220 and 4230, respectively. At block 4740,the determination of an estimated location for the mobile device mayfurther include directly evaluating a joint probability (i.e., as anaggregate rather than through computation of the product of marginalpdfs) of power levels for at least one cell neighboring a cell servingthe mobile device in the one NMR over a set of available grid points inthe grid. At block 4750, an estimated location of the mobile device maybe generated that may comprise a highest joint probability matching gridpoint in the grid, a weighted sum of the locations of a set of matchinggrid points in the grid (exemplary weights may be defined by any numberof means such as, but not limited to, a joint probability derived fromindividual pdfs, etc.), a weighted sum of clustered locations of a setof matching grid points in the grid (i.e., cluster the locations of thematching grid points and then apply a cumulative probability for allcontained grid points in a cluster as a weight for the respectivecluster), and as a function of locations determined from subsets of theplurality of NMRs. At block 4760, a further embodiment may also filteravailable grid points as a function of any one or combination ofselected characteristics, such as, but not limited to, a matching ofserving cell or sector for grid points (provided such a characterizationis available for the respective grid points) in the grid to the servingcell or sector of the one NMR, a TA parameter from the one NMR, a RTTparameter from the one NMR, a mobile device orientation parameter, aserving cell identifier, a serving control channel, a magnitude of aserving cell forward link control channel received signal level, apredetermined number of cell received signal levels common to the oneNMR, or any combination thereof.

FIG. 48 is a flow chart for a method of locating a mobile device in ageographic region according to yet another embodiment of the presentsubject matter. With reference to FIG. 48, blocks 4810, 4820 and 4830are similar to blocks 4210, 4220 and 4230, respectively. At block 4840,the determination of an estimated location for the mobile device mayfurther include determining a distortion measure between acharacteristic function or parameter of a grid point and a correspondingfunction or parameter obtained for the one NMR. At block 4850, anestimated location of the mobile device may be generated that maycomprise a location of a grid point having the smallest distortionmeasure, a weighted sum of the locations of a set of matching gridpoints in the grid (where the weighting applied to each of the matchinggrid points may be a function of the distortion measure), a weighted sumof clustered locations of a set of matching grid points in the grid, asa function of locations determined from subsets of the plurality ofNMRs, or any combination thereof. At block 4860, a further embodimentmay also filter available grid points as a function of any one orcombination of selected characteristics, such as, but not limited to, amatching of serving cell or sector for grid points (provided such acharacterization is available for the respective grid points) in thegrid to the serving cell or sector of the one NMR, a TA parameter fromthe one NMR, a RTT parameter from the one NMR, a mobile deviceorientation parameter, a serving cell identifier, a serving controlchannel, a magnitude of a serving cell forward link control channelreceived signal level, a predetermined number of cell received signallevels common to the one NMR, or any combination thereof. In anadditional embodiment, the distortion measure may be, but is not limitedto, a Mahalanobis distance.

FIG. 49 is a flow chart for a method of locating a mobile device in ageographic region according to yet another embodiment of the presentsubject matter. With reference to FIG. 49, blocks 4910, 4920 and 4930are similar to blocks 4210, 4220 and 4230, respectively. At block 4940,the determination of an estimated location for the mobile device mayfurther include matching cell power ordering of cells neighboring a cellserving the mobile device in the one NMR to neighboring cell powerordering in each of the grid points in the grid. At block 4950, anestimated location may then be selected as a function of a quality ofthe matching. The quality may be a function of a relative shift in theordering sequence occurring between the one NMR and grid point cellpower ordering. It is also envisioned that this same concept may beapplied to any other vector parameter characterizing NMRs and/or gridpoints and such an example should not limit the scope of the claimsappended herewith. At block 4960, a further embodiment may also filteravailable grid points as a function of any one or combination ofselected characteristics, such as, but not limited to, a matching ofserving cell or sector for grid points (provided such a characterizationis available for the respective grid points) in the grid to the servingcell or sector of the one NMR, a TA parameter from the one NMR, a RTTparameter from the one NMR, a mobile device orientation parameter, aserving cell identifier, a serving control channel, a magnitude of aserving cell forward link control channel received signal level, apredetermined number of cell received signal levels common to the oneNMR, or any combination thereof.

FIG. 50 is a flow chart for another method of locating a mobile devicein a geographic region according to an embodiment of the present subjectmatter. With reference to FIG. 50, at block 5010, a plurality of gridpoints may be provided in a geographic region where each of the gridpoints may include at least one characterizing parameter and each of thegrid points may be located on a grid defined over the geographic region.At block 5020, a plurality of NMRs may be provided for a mobile devicein the geographic region, and at block 5030, an estimated location maybe determined for the mobile device from a set of said plurality ofnetwork measurement reports as a function of the parameter. Of course,one or more of the grid points may be randomly located within thegeographic region, and one or more of the grid points may be located ona predetermined fixed uniform grid defined over the geographic region.

FIG. 51 is a flow chart for another method of locating a mobile devicein a geographic region according to an embodiment of the present subjectmatter. With reference to FIG. 51, blocks 5110, 5120 and 5130 aresimilar to blocks 5010, 5020 and 5030, respectively. At block 5140, thedetermination of an estimated location for the mobile device may alsoinclude determining a cluster for each NMR characteristic or parameter,clustered by location, in the set of NMRs. The clustering may further beweighted by an exemplary metric that may be, but is not limited to, aMahalanobis distance, joint probability, probability density function,and any combination thereof.

FIG. 52 is a flow chart for another method of locating a mobile devicein a geographic region according to an additional embodiment of thepresent subject matter. With reference to FIG. 52, blocks 5210, 5220 and5230 are similar to blocks 5010, 5020 and 5030, respectively. At block5240, the determination of an estimated location for the mobile devicemay include determining a representative value for each parameteroccurring in the set of NMRs. At block 5250, a cluster for eachrepresentative value may also be weighted by an exemplary metric (e.g.,a Mahalanobis distance, joint probability, probability density function,etc.) to weight the cluster. An exemplary representative value may be,but is not limited to, serving cell power level, neighboring cell powerlevel, timing advance, round trip time, or any combination thereof.Further, the representative value may be determined as a function of amean or median of a set of parameter values obtained over the set ofNMRs.

FIG. 53 is a flow chart for another method of locating a mobile devicein a geographic region according to a further embodiment of the presentsubject matter. With reference to FIG. 53, blocks 5310, 5320 and 5330are similar to blocks 5010, 5020 and 5030, respectively. At block 5340,the determination of an estimated location for the mobile device mayalso include observing a transition in a parameter occurring in one ormore of the NMRs within an applicable set of NMRs. At block 5350, alocation of the mobile device may be estimated on a boundary defined bya first range represented by the parameter before the transition and bya second range represented by the parameter after the transition. Anexemplary parameter may be, but is not limited to, signal power level,signal quality, rate of dropping in/out of a neighboring cell signal,pattern of dropping in/out of a neighboring cell signal, changes inserving cell, changes in serving sector, RTT, TA, and any combinationsthereof.

FIG. 54 is a flow chart for another method of locating a mobile devicein a geographic region according to an additional embodiment of thepresent subject matter. With reference to FIG. 54, blocks 5410, 5420 and5430 are similar to blocks 5010, 5020 and 5030, respectively. At block5440, the determination of an estimated location for the mobile devicemay also determine a distortion measure between a parameter or functionof ones of the grid points and a corresponding parameter or function ineach NMR in the set. At block 5450, an estimated location of the mobiledevice may be generated where the estimated location may be, but is notlimited to, a location of the grid point having the smallest distortionmeasure, a weighted sum of the locations of a set of matching gridpoints in the grid (where the weighting applied to each grid point maybe a function of the distortion measure), a weighted sum of clusteredlocations of a set of matching grid points in the grid (i.e., clusterthe locations of the matching grid points and then apply a cumulativeprobability for all contained grid points in a cluster as a weight forthe respective cluster), as a function of estimated locations determinedfrom subsets of the set of NMRs by the preceding methods, and anycombination thereof. An exemplary distortion measure may be, but is notlimited to, a Mahalanobis distance.

FIG. 55 is a flow chart for another method of locating a mobile devicein a geographic region according to yet another embodiment of thepresent subject matter. With reference to FIG. 55, blocks 5510, 5520 and5530 are similar to blocks 5010, 5020 and 5030, respectively. At block5540, the determination of an estimated location for the mobile devicemay determine a representative value for each cell neighboring a servingcell serving the mobile device in the set of NMRs. More generally, foreach parameter type held in common by an non-empty subset of the NMRs, arepresentative value may be generated. At block 5550, a distortionmeasure for each representative value may be determined as a function ofa comparison between a parameter of ones of the grid points and acorresponding parameter for each representative value. At block 5560, anestimated location of the mobile device may be generated where theestimated location may comprise a location of the grid point having thesmallest overall distortion measure (computed over all parameters of theset of NMRs), a weighted sum of the locations of a set of matching gridpoints in the grid (where the weighting may be a function of thedistortion measure), a weighted sum of clustered locations of a set ofmatching grid points in the grid, as a function of estimated locationsdetermined from subsets of the set of NMRs by the preceding methods, orany combination thereof.

As shown by the various configurations and embodiments illustrated inFIGS. 1-55, a method and system for generating a location estimate usingnon-uniform grid points have been described.

While preferred embodiments of the present subject matter have beendescribed, it is to be understood that the embodiments described areillustrative only and that the scope of the invention is to be definedsolely by the appended claims when accorded a full range of equivalence,many variations and modifications naturally occurring to those of skillin the art from a perusal hereof.

1. A method of locating a mobile device in a geographic regioncomprising the steps of: (a) providing a plurality of grid points in ageographic region, each of said grid points including at least onecharacterizing parameter and each of said grid points located on a griddefined over said geographic region; (b) providing a plurality ofnetwork measurement reports for a mobile device in said geographicregion; and (c) determining an estimated location for said mobile devicefrom one network measurement report as a function of said at least oneparameter.
 2. The method of claim 1 wherein one of said grid points israndomly located within said geographic region.
 3. The method of claim 1wherein one of said grid points is located on a predetermined fixeduniform grid defined over said geographic region.
 4. The method of claim1 wherein the step of determining an estimated location for said mobiledevice comprises: (i) comparing an ordered list of cells neighboring acell serving said mobile device in said one network measurement reportto an ordered list of neighboring cells in each grid point of said grid,said ordering being a function of at least one parameter of said onenetwork measurement report; and (ii) generating an estimated location ofsaid mobile device wherein said estimated location is selected from thegroup consisting of: a centroid of a cluster of matching grid points insaid grid; a highest joint probability matching grid point in said grid;a weighted sum of the locations of a set of matching grid points in saidgrid; a weighted sum of clustered locations of a set of matching gridpoints in said grid; and as a function of estimated locations determinedfrom subsets of said plurality of network measurement reports.
 5. Themethod of claim 4 further comprising filtering available grid points asa function of one characteristic selected from the group consisting of:a matching of serving cell or sector for available grid points in saidgrid to the serving cell or sector of said one network measurementreport; a timing advance parameter from said one network measurementreport; a round trip time parameter from said one network measurementreport; a mobile device orientation parameter; a serving cellidentifier; a serving control channel; a magnitude of a serving cellforward link control channel received signal level; a predeterminednumber of cell received signal levels common to said one networkmeasurement report; and any combination thereof.
 6. The method of claim1 wherein the step of determining an estimated location for said mobiledevice comprises: (i) comparing an ordered list of cells neighboring acell serving said mobile device in said one network measurement reportto an ordered list of neighboring cells in each grid point of said grid,said ordering being a function of at least one parameter of said onenetwork measurement report; (ii) if no exact match is made between theordered list of neighboring cells of said one network measurement reportand any grid point of the grid then forming a largest subset of theordered list of neighboring cells in said grid points that provide amatch; and (iii) generating an estimated location of said mobile devicewherein said estimated location is selected from the group consistingof: a centroid of a cluster of matching grid points from said subset; ahighest joint probability matching grid point from said subset; aweighted sum of the locations of a set of matching grid points in saidsubset; a weighted sum of clustered locations of a set of matching gridpoints in said subset; and as a function of estimated locationsdetermined from subsets of said plurality of network measurementreports.
 7. The method of claim 6 further comprising filtering availablegrid points as a function of one characteristic selected from the groupconsisting of: a matching of serving cell or sector for available gridpoints in said grid to the serving cell or sector of said one networkmeasurement report; a timing advance parameter from said one networkmeasurement report; a round trip time parameter from said one networkmeasurement report; a mobile device orientation parameter; a servingcell identifier; a serving control channel; a magnitude of a servingcell forward link control channel received signal level; a predeterminednumber of cell received signal levels common to said one networkmeasurement report; and any combination thereof.
 8. The method of claim1 wherein the step of determining an estimated location for said mobiledevice comprises: (i) comparing an ordered list of cells neighboring acell serving said mobile device in said one network measurement reportto an ordered list of neighboring cells in each grid point of said grid,said ordering being a function of at least one parameter of said onenetwork measurement report; (ii) if the ordered list of neighboringcells of said one network measurement report is not contained in theordered list of neighboring cells for said grid then using a largestsubset of ordered neighboring cells in said one network measurementreport having either an exact match or contained in the ordered list ofneighboring cells in said grid; and (iii) generating an estimatedlocation of said mobile device wherein said estimated location isselected from the group consisting of: a centroid of a cluster ofmatching grid points in said grid; a highest joint probability matchinggrid point in said grid; a weighted sum of the locations of a set ofmatching grid points in said grid; a weighted sum of clustered locationsof a set of matching grid points in said grid; and as a function ofestimated locations determined from subsets of said plurality of networkmeasurement reports.
 9. The method of claim 8 further comprisingfiltering available grid points as a function of one characteristicselected from the group consisting of: a matching of serving cell orsector for available grid points in said grid to the serving cell orsector of said one network measurement report; a timing advanceparameter from said one network measurement report; a round trip timeparameter from said one network measurement report; a mobile deviceorientation parameter; a serving cell identifier; a serving controlchannel; a magnitude of a serving cell forward link control channelreceived signal level; a predetermined number of cell received signallevels common to said one network measurement report; and anycombination thereof.
 10. The method of claim 1 wherein the step ofdetermining an estimated location for said mobile device comprises: (i)evaluating a probability density function for each power level of a cellneighboring a cell serving said mobile device in said one networkmeasurement report over each grid point of a set of available gridpoints in said grid; (ii) determining a joint probability as a functionof said individual probability density functions; and (iii) generatingan estimated location of said mobile device wherein said estimatedlocation is selected from the group consisting of: a highest jointprobability matching grid point in said grid; a weighted sum of thelocations of a set of matching grid points in said grid; a weighted sumof clustered locations of a set of matching grid points in said grid;and as a function of estimated locations determined from subsets of saidplurality of network measurement reports.
 11. The method of claim 10further comprising filtering available grid points as a function of onecharacteristic selected from the group consisting of: a matching ofserving cell or sector for available grid points in said grid to theserving cell or sector of said one network measurement report; a timingadvance parameter from said one network measurement report; a round triptime parameter from said one network measurement report; a mobile deviceorientation parameter; a serving cell identifier; a serving controlchannel; a magnitude of a serving cell forward link control channelreceived signal level; a predetermined number of cell received signallevels common to said one network measurement report; and anycombination thereof.
 12. The method of claim 1 wherein the step ofdetermining an estimated location for said mobile device comprises: (i)evaluating a joint probability of power levels for at least one cellneighboring a cell serving said mobile device in said one networkmeasurement report over a set of available grid points in said grid; and(ii) generating an estimated location of said mobile device wherein saidestimated location is selected from the group consisting of: a highestjoint probability matching grid point in said grid; a weighted sum ofthe locations of a set of matching grid points in said grid; a weightedsum of clustered locations of a set of matching grid points in saidgrid; and as a function of estimated locations determined from subsetsof said plurality of network measurement reports.
 13. The method ofclaim 12 further comprising filtering available grid points as afunction of one characteristic selected from the group consisting of: amatching of serving cell or sector for available grid points in saidgrid to the serving cell or sector of said one network measurementreport; a timing advance parameter from said one network measurementreport; a round trip time parameter from said one network measurementreport; a mobile device orientation parameter; a serving cellidentifier; a serving control channel; a magnitude of a serving cellforward link control channel received signal level; a predeterminednumber of cell received signal levels common to said one networkmeasurement report; and any combination thereof.
 14. The method of claim1 wherein the step of determining an estimated location for said mobiledevice comprises: (i) determining a distortion measure between aparameter or a function characterizing a grid point and a correspondingparameter or function in said one network measurement report; and (ii)generating an estimated location of said mobile device wherein saidestimated location is selected from the group consisting of: a locationof a grid point having the smallest distortion measure; a weighted sumof the locations of a set of matching grid points in said grid, saidweighted sum being a function of said distortion measure; a weighted sumof clustered locations of a set of matching grid points in said grid;and as a function of estimated locations determined from subsets of saidplurality of network measurement reports.
 15. The method of claim 14further comprising filtering available grid points as a function of onecharacteristic selected from the group consisting of: a matching ofserving cell or sector for available grid points in said grid to theserving cell or sector of said one network measurement report; a timingadvance parameter from said one network measurement report; a round triptime parameter from said one network measurement report; a mobile deviceorientation parameter; a serving cell identifier; a serving controlchannel; a magnitude of a serving cell forward link control channelreceived signal level; a predetermined number of cell received signallevels common to said one network measurement report; and anycombination thereof.
 16. The method of claim 14 wherein said distortionmeasure is a Mahalanobis distance.
 17. The method of claim 1 wherein thestep of determining an estimated location for said mobile devicecomprises: (i) matching cell parameter ordering of cells neighboring acell serving said mobile device in said one network measurement reportto neighboring cell parameter ordering in each of the grid points insaid grid; and (ii) selecting an estimated location as a function of aquality of said matching, wherein said quality is a function of arelative shift in an ordering sequence occurring between said onenetwork measurement report and grid point cell parameter ordering. 18.The method of claim 17 further comprising filtering available gridpoints as a function of one characteristic selected from the groupconsisting of: a matching of serving cell or sector for available gridpoints in said grid to the serving cell or sector of said one networkmeasurement report; a timing advance parameter from said one networkmeasurement report; a round trip time parameter from said one networkmeasurement report; a mobile device orientation parameter; a servingcell identifier; a serving control channel; a magnitude of a servingcell forward link control channel received signal level; a predeterminednumber of cell received signal levels common to said one networkmeasurement report; and any combination thereof.
 19. The method of claim17 wherein said parameter is selected from the group consisting of: anordered list of neighboring cells; functions defined on the absoluteneighboring cell power levels; functions defined on the relativeneighboring cell power differences; serving cell; serving sector; timingadvance parameter; round trip time parameter; individual probabilitydensity function of each neighboring cell power level; individualprobability distribution function of each neighboring cell power level;joint probability density function of neighboring cell power levels;joint probability distribution function of neighboring cell powerlevels; mean and variance of neighboring cell power levels; mobiledevice orientation; a representation that enables retrieval ofcalibration data NMR vectors assigned to a respective NUG; network stateas indicated in the calibration data; a confidence measure indicative ofthe reliability of the calibration data feeding a respective NUG; andany combinations thereof.
 20. A method of locating a mobile device in ageographic region comprising the steps of: (a) providing a plurality ofgrid points in a geographic region, each of said grid points includingat least one characterizing parameter and each of said grid pointslocated on a grid defined over said geographic region; (b) providing aplurality of network measurement reports for a mobile device in saidgeographic region; and (c) determining an estimated location for saidmobile device from a set of said plurality of network measurementreports as a function of said parameter.
 21. The method of claim 20wherein one of said grid points is randomly located within saidgeographic region.
 22. The method of claim 20 wherein one of said gridpoints is located on a predetermined fixed uniform grid defined oversaid geographic region.
 23. The method of claim 20 wherein the step ofdetermining an estimated location for said mobile device furthercomprises: (i) determining a cluster for each parameter in each networkmeasurement report in said set of network measurement reports as afunction of a metric to weight said cluster.
 24. The method of claim 23wherein said metric is selected form the group consisting of:Mahalanobis distance, joint probability, probability density function,parameter quality, and any combination thereof.
 25. The method of claim20 wherein the step of determining an estimated location for said mobiledevice further comprises: (i) determining a representative value foreach parameter or function occurring in said set of network measurementreports; and (ii) determining a cluster for each representative value asa function of a metric to weight said cluster.
 26. The method of claim25 wherein said representative value is selected from the groupconsisting of: power level; neighboring cell power level; quality ofmeasurements; timing advance; round trip time; or any combinationthereof.
 27. The method of claim 25 wherein said representative value isdetermined as a function of a mean of a set of parameter values.
 28. Themethod of claim 25 wherein said representative value is determined as afunction of a median of a set of parameter values.
 29. The method ofclaim 20 wherein the step of determining an estimated location for saidmobile device further comprises: (i) observing a transition in aparameter occurring in one or more network measurement reports withinsaid set of network measurement reports; and (ii) estimating a locationof said mobile device on a boundary defined by a first range representedby said parameter before said transition and by a second rangerepresented by said parameter after said transition.
 30. The method ofclaim 29 wherein said parameter is selected from the group consistingof: signal power level; rate of dropping in/out of a neighboring cellsignal; pattern of dropping in/out of a neighboring cell signal; signalquality; changes in serving cell; changes in serving sector; timingadvance; round trip time; and combinations thereof.
 31. The method ofclaim 20 wherein the step of determining an estimated location for saidmobile device comprises: (i) determining a distortion measure between aparameter or function of ones of said grid points and a correspondingparameter or function in each network measurement report in said set;and (ii) generating an estimated location of said mobile device whereinsaid estimated location is selected from the group consisting of: alocation of the grid point having the smallest distortion measure; aweighted sum of the locations of a set of matching grid points in saidgrid, said weighted sum being a function of said distortion measure; aweighted sum of clustered locations of a set of matching grid points insaid grid; and as a function of estimated locations determined fromsubsets of said set of network measurement reports.
 32. The method ofclaim 31 wherein said distortion measure is a Mahalanobis distance. 33.The method of claim 20 wherein the step of determining an estimatedlocation for said mobile device comprises: (i) determining one or morerepresentative values for each cell neighboring a serving cell servingsaid mobile device in said set of network measurement reports; (ii)determining a distortion measure for each representative value as afunction of a comparison between a selected parameter of ones of saidgrid points and a corresponding parameter for said each representativevalue; and (iii) generating an estimated location of said mobile devicewherein said estimated location is selected from the group consistingof: a location of the grid point having the smallest distortion measure;a weighted sum of the locations of a set of matching grid points in saidgrid, said weighted sum being a function of said distortion measure; aweighted sum of clustered locations of a set of matching grid points insaid grid; and as a function of estimated locations determined fromsubsets of said set of network measurement reports.